PATENT DOCUMENT

Publication Number: US-8705811-B1
Application Number: US-91262010-A
Country: US
Kind Code: B1

Title: Luminance adjusted face detection

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for adjusting luminance of a candidate window prior to facial detection processing. In one aspect, a method includes establishing a target region of an image that potentially contains at least a portion of a face. The method further includes establishing an inset region inside the established target region. The inset region is sized to include a predetermined fraction of the target region. Furthermore, the method includes detecting a face within the established target region of the image using the established inset region.

Claims:
What is claimed is: 
     
       1. A method performed by data processing apparatus, the method comprising:
 establishing a combination of color space component values weighted to generate skin-tone bias; 
 converting color data associated with an image to skin-tone-biased luminance data in accordance with the established combination to generate a color-converted image; 
 establishing a target region of the color-converted image that potentially contains at least a portion of a face; 
 establishing an inset region inside the established target region, the inset region being sized to include a predetermined fraction of the target region; 
 normalizing a luminance variance of the inset region to obtain a normalization scale; 
 normalizing a luminance variance of the target region based on the obtained normalization scale; and 
 detecting a face within the established target region using the established inset region. 
 
     
     
       2. The method of  claim 1 , wherein the predetermined fraction of the target region included in the inset region is larger than a first fractional value. 
     
     
       3. The method of  claim 2 , wherein the predetermined fraction of the target region included in the inset region is less than a second fractional value. 
     
     
       4. The method of  claim 1 , wherein the inset region corresponds to a convex portion of the target region, the portion having a size equal to the predetermined fraction of the target region. 
     
     
       5. The method of  claim 4 , wherein the portion of the target region corresponding to the inset region is sized and positioned relative to the target region such that, when the target region inscribes a face, the inset region is (i) sufficiently large to include at least eyes, a nose and a mouth of the inscribed face, and is (ii) sufficiently small to exclude portions of the established target region that are not part of the inscribed face. 
     
     
       6. The method of  claim 1 , wherein the inset region corresponds to portions of the target region excluding outlier values of luminance, the portions having a cumulative size equal to the predetermined fraction of the target region. 
     
     
       7. The method of  claim 1 , wherein the act of establishing a combination of color space component values comprises determining a value, alpha, for use in the equation: alpha*Y+(1-alpha)*Cr, wherein Y refers to luminance value and Cr refers to red chrominance value. 
     
     
       8. The method of  claim 1 , wherein the act of obtaining a normalization scale further comprises dividing a dynamic range of luminance for the image by a dynamic range of the inset region. 
     
     
       9. The method of  claim 1 , wherein the act of obtaining a normalization scale further comprises analyzing a histogram representing a distribution of luminance among pixels of the inset region. 
     
     
       10. An appliance comprising:
 a memory configured to store an image; and 
 a processor communicatively coupled with the memory and configured to perform operations comprising:
 establishing a combination of color space component values weighted to generate skin-tone bias; 
 converting color data associated with the image to skin-tone-biased luminance data in accordance with the established combination to generate a color-converted image; 
 establishing a target region of the color-converted image that potentially contains at least a portion of a face; 
 establishing an inset region inside the established target region, the inset region being sized to include a predetermined fraction of the target region; 
 normalizing a luminance variance of the inset region to obtain a normalization scale; 
 normalizing a luminance variance of the target region based on the obtained normalization scale; and 
 detecting a face within the established target region using the established inset region. 
 
 
     
     
       11. The appliance of  claim 10 , wherein the predetermined fraction of the target region included in the inset region is larger than a first fractional value. 
     
     
       12. The appliance of  claim 11 , wherein the predetermined fraction of the target region included in the inset region is less than a second fractional value. 
     
     
       13. The appliance of  claim 10 , wherein the inset region corresponds to a convex portion of the target region such that the portion has a size equal to the predetermined fraction of the target region. 
     
     
       14. The appliance of  claim 10 , wherein the inset region corresponds to portions of the target region excluding outlier values of luminance such that the portions have a cumulative size equal to the predetermined fraction of the target region. 
     
     
       15. The appliance of  claim 10 , wherein the operation of establishing a combination of color space component values comprises determining a value, alpha, for use in the equation: alpha*Y+(1-alpha)*Cr, wherein Y refers to luminance value and Cr refers to red chrominance value. 
     
     
       16. The appliance of  claim 10 , wherein the operation of obtaining a normalization scale further comprises dividing a dynamic range of luminance for the image by a dynamic range of the inset region. 
     
     
       17. The appliance of  claim 10 , wherein the operation of obtaining a normalization scale further comprises analyzing a histogram representing a distribution of luminance among pixels of the inset region. 
     
     
       18. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:
 establishing a combination of color space component values weighted to generate skin-tone bias; 
 converting color data associated with an image to skin-tone-biased luminance data in accordance with the established combination to generate a color-converted image; 
 establishing a target region of the color-converted image that potentially contains at least a portion of a face; 
 establishing an inset region inside the established target region, the inset region being sized to include a predetermined fraction of the target region; 
 normalizing a luminance variance of the inset region to obtain a normalization scale; 
 normalizing a luminance variance of the target region based on the obtained normalization scale; and 
 detecting a face within the established target region using the established inset region. 
 
     
     
       19. The computer storage medium of  claim 18 , wherein the predetermined fraction of the target region included in the inset region is larger than a first fractional value. 
     
     
       20. The computer storage medium of  claim 19 , wherein the predetermined fraction of the target region included in the inset region is less than a second fractional value. 
     
     
       21. The computer storage medium of  claim 18 , wherein the inset region corresponds to a convex portion of the target region, the portion having a size equal to the predetermined fraction of the target region. 
     
     
       22. The computer storage medium of  claim 18 , wherein the inset region corresponds to portions of the target region excluding outlier values of luminance, the portions having a cumulative size equal to the predetermined fraction of the target region. 
     
     
       23. The computer storage medium of  claim 18 , wherein the operation of establishing a combination of color space component values comprises determining a value, alpha, for use in the equation: alpha*Y+(1-alpha)*Cr, wherein Y refers to luminance value and Cr refers to red chrominance value. 
     
     
       24. The computer storage medium of  claim 18 , wherein the operation of obtaining a normalization scale further comprises dividing a dynamic range of luminance for the image by a dynamic range of the inset region. 
     
     
       25. The computer storage medium of  claim 18 , wherein the operation of obtaining a normalization scale further comprises analyzing a histogram representing a distribution of luminance among pixels of the inset region. 
     
     
       26. A method performed by data processing apparatus, the method comprising:
 establishing a combination of color space component values weighted to generate skin-tone bias; 
 converting color data associated with an image to skin-tone-biased luminance data in accordance with the established combination to generate a color-converted image; 
 establishing a target region of the color-converted image that potentially contains at least a portion of a face; 
 establishing an inset region inside the established target region, the inset region being sized to include a predetermined fraction of the target region, the predetermined fraction being larger than a first fractional value and smaller than a second fractional value; 
 normalizing a luminance variance of the inset region to obtain a normalization scale; 
 normalizing a luminance variance of the target region based on the obtained normalization scale; and 
 detecting a face within the target region using the established inset region. 
 
     
     
       27. The method of  claim 26 , wherein the inset region corresponds to a rectangular portion of the target region, the rectangular portion having a size equal to the predetermined fraction of the target region. 
     
     
       28. The method of  claim 26 , wherein the act of establishing a combination of color space component values comprises determining a value, alpha, for use in the equation: alpha*Y+(1-alpha)*Cr, wherein Y refers to luminance value and Cr refers to red chrominance value. 
     
     
       29. The method of  claim 26 , wherein the act of obtaining a normalization scale further comprises dividing a dynamic range of luminance for the image by a dynamic range of the inset region. 
     
     
       30. The method of  claim 26 , wherein the act of obtaining a normalization scale further comprises analyzing a histogram representing a distribution of luminance among pixels of the inset region.

Description:
BACKGROUND 
     This specification relates to face detection, and specifically to luminance adjusted face detection. 
     Digital images are being used in increasingly more applications. In many of those applications, automated analysis of digital images can be performed to provide either or both of face detection and face recognition. In face detection, an image region is identified as depicting the face of a human (or other) being. In face recognition, a detected face is identified as corresponding to a specific, known individual. Face detection and face recognition can be used for a wide variety of tasks, including image enhancement, content-based retrieval, automatic identification, and image database management. For instance, in image processing applications, face detection can be used to automatically perform enhancements, such as red-eye correction and contrast adjustment. Further, face recognition can be used in conjunction with search applications that retrieve images depicting a particular individual. 
     Real-time face detection algorithms include, for example, the Viola-Jones algorithm, have been developed for performing face detection in digital images. The Viola-Jones algorithm searches pixels of a candidate window using multiple classifiers, with each classifier configured to select particular visual features (e.g., eyes, nose, and mouth) from a set of possible visual features. Further, the classifiers are grouped in stages, which are cascaded. As a result, only candidate windows that pass the classifiers of the current stage are submitted for further analysis to the classifiers of a subsequent stage. 
     SUMMARY 
     This specification describes, among other things, technologies relating to adjusting luminance of a candidate window prior to facial detection processing. A luminance variance can be normalized within a candidate window to enhance contrast of an analyzed region of the image, and thus, to improve the likelihood for success of a classifier-based facial detection process subsequently applied to the candidate window having normalized luminance variance. 
     In general, one aspect of the subject matter described in this specification can be implemented in methods that include the actions of establishing a target region of an image that potentially contains at least a portion of a face. The methods further include establishing an inset region inside the established target region. The inset region is sized to include a predetermined fraction of the target region. Furthermore, the methods include detecting a face within the established target region of the image using the established inset region. 
     Implementations can optionally include one or more of the following features. Prior to detecting, the methods can further include normalizing a luminance variance of the inset region to obtain a normalization scale, and normalizing a luminance variance of the target region of the image based on the obtained normalization scale. Before establishing the target region and the inset region, the methods can also include establishing a combination of color space component values weighted to generate skin-tone bias, and converting color data associated with the image to skin-tone-biased luminance data in accordance with the established combination. Normalizing the luminance variance of the inset region, normalizing the luminance variance of the target region, and detecting the face can be performed on the converted skin-tone-biased luminance data. 
     In some implementations, the predetermined fraction of the target region of the image included in the inset region is larger than a first fractional value. Further, the predetermined fraction of the target region of the image included in the inset region can be less than a second fractional value. For example, the first and second fractional values can be 80% and 90%, respectively. As another example, the predetermined fraction of the target region of the image included in the inset region can be substantially 85%. 
     In some implementations, the inset region can correspond to a convex portion of the target region of the image, such that the portion has a size equal to the predetermined fraction of the target region. In some implementations, the portion of the target region of the image corresponding to the inset region can be sized and positioned relative to the target region such that, when the target region may inscribe a face, the inset region is (i) sufficiently large to include at least eyes, a nose and a mouth of the inscribed face, and is (ii) sufficiently small to exclude portions of the established target region that are not part of the inscribed face. 
     In some implementations, the inset region can correspond to portions of the target region of the image excluding outlier values of luminance, such that the portions have a cumulative size equal the predetermined fraction of the target region. 
     Another aspect of the subject matter described in this specification can be implemented in methods that include the actions of establishing a combination of color space component values weighted to generate skin-tone bias. The methods also include converting color data associated with an image to skin-tone-biased luminance data in accordance with the established combination. Further, the methods include establishing a target region of the image that potentially contains at least a portion of a face, and normalizing a variance of the converted skin-tone-biased luminance data associated with the target region of the image. In addition, the methods include detecting a face within the target region of the image after said normalizing. 
     Implementations can optionally include one or more of the following features. The methods can include establishing an inset region inside the established target region, such that the inset region is sized to include a predetermined fraction of the target region. The methods can further include normalizing a luminance variance of the converted skin-tone-biased data of the inset region to obtain a normalization scale. Normalizing the variance of the converted skin-tone-biased luminance data associated with the target region of the image can be performed in accordance with the obtained normalization scale. 
     The subject matter described in this specification can be implemented as a method or as a system or using computer program products, tangibly embodied in information carriers, such as a CD-ROM, a DVD-ROM, a HD-DVD-ROM, a Blue-Ray drive, a semiconductor memory, and a hard disk. Such computer program products may cause a data processing apparatus to conduct one or more operations described in this specification. 
     The subject matter described in this specification can also be implemented as an appliance including a processor and a memory coupled to the processor. The memory may encode one or more programs that cause the processor to perform one or more of the method acts described in this specification. Further the subject matter described in this specification can be implemented using various data processing machines. 
     Particular implementations of the subject matter described in this specification can be configured so as to realize one or more of the following potential advantages. By normalizing luminance variance within a candidate window potentially depicting a face based on normalization scaling corresponding to a corresponding inset region, pixels from outside a face are prevented from contributing to, and possibly skewing, the normalization process. 
     The details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and potential advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is an input/output diagram of an example process for detecting faces in digital images. 
         FIG. 1B  is a flow chart of an example method for detecting a portion of a face depicted in an image. 
         FIGS. 2A ,  2 B and  2 C show aspects of an example method for normalizing a luminance variance inside a candidate window. 
         FIG. 3  shows experimental results obtained based on methods described in this specification. 
         FIG. 4  is a flow chart of an example method for detecting a portion of a face depicted in a color image. 
         FIG. 5  shows additional experimental results obtained based on the methods described in this specification. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     Techniques and systems disclosed in this specification can be implemented, among other things, to determine a scale for normalizing the luminance variance within a candidate window based on luminance data inside an inset rectangle, thereby increasing the contrast on the person&#39;s face. As a result, contributions from areas near the edges of the candidate window that are outside the person&#39;s face are minimized. The relative size of the inset rectangle (to the candidate window) can be determined experimentally. 
     Further, the techniques and systems disclosed in this specification can be implemented to perform the normalization of the luminance variance inside the candidate window after eliminating outlier luminance data. This is equivalent to determining the scale for normalizing the luminance variance within the candidate window based on luminance data inside an inset region of the candidate window such that the inset region has a predetermined relative size (to the candidate window) and excludes pixels corresponding to luminance outliers. 
     Furthermore, the techniques and systems disclosed in this specification can be implemented to perform a particular conversion of image data from color to grey-scale to bias the image&#39;s luminance towards skin-tone. In this manner, the contrast of face-specific features can be selectively pre-enhanced, even before normalizing the luminance variance. For example, a linear combination alpha*Y+(1-alpha)*Cr can be used (for images in YCbCr space), with alpha being in the range of 0.5-0.7 Skin-tone-biased data within candidate windows can then be normalized using either data from entire candidate windows or from respective inset regions of the candidate windows. 
       FIG. 1A  is an input/output diagram of an example process  100  for detecting faces in digital images using candidate windows which have an inset region for normalizing corresponding luminance variance. The process  100  can be implemented on numerous electronic devices with digital sensors, such as digital cameras, mobile phones, and the like. Throughout this specification, such devices are referred to as data processing apparatus. 
     An input of the process  100  can be a digital image  10 . The digital image  10  depicts a scene including what appears to be a door frame  30  delimiting an inside/indoors part of the scene and an outside/outdoors part of the scene. The indoors-part of the scene includes what appears to be a human subject  20 . While the background  40  included in the outdoors-part of the scene is bright, the door frame  30  and the human subject  20  are dark, potentially due to daylight outdoors and a poorly lit scene indoors. Since the contrast is poor in the region of the image  10  where the human subject  20  is represented, process  100  can be applied to determine whether the human subject  20  faces inwards (towards the camera) or outwards (away from the camera.) 
     An output of the process  100  can be a processed digital image  10 ′. In this example, the process  100  has detected a face  50  of the human subject  20 ′ facing the camera. Further in this example, the original image  10  has been further processed to adjust the exposure of the entire processed image  10 ′ based on optimizing a luminance median and range of the detected face  50 . Consequently, the human subject  20 ′, the door frame  30 ′ and even the background  40 ′ also have been adjusted based on the optimized luminance corresponding to the detected face  50  of the human subject  20 ′. Further processing can be applied to the processed image  10 ′. For example, a face recognition process can be applied to image  10 ′ to determine an identity of the detected face  50 . 
     The process  100  for face detection can be based on a Viola Jones algorithm described above. However, other facial detection algorithms that are known in the art, e.g., Rowley Baluja Kanade, or Schneiderman Kanade, may be chosen to perform real-time face detection over the image  10 . 
     In some implementations of process  100 , color image data is first converted to grey-scale within an image space YCrCb. “Y” represents luminance data, while (Cr, Cb) represent measures of chrominance. For example, luminance Y can be expressed in terms of an 8-bit grey-scale (with level-0 corresponding to black and level-511 corresponding to white.) In this example, the dynamic range of the luminance Y is 512 grey levels. 
     Candidate windows used by the process  100  can be placed over a given region of the image  10  to detect a face within the given region. As described above, a candidate window can be iteratively moved through all regions of the image  10  for identifying faces. For each rectangle position, the grey-scale image data (corresponding to luminance Y) is normalized prior to detecting a face in an attempt to reduce the influence of luminance changes across the image. Normalization within a candidate window can include scaling the luminance variance to 1 (corresponding to the luminance&#39;s dynamic range.) Accordingly, the normalized luminance variance can provide full dynamic range to subsequently applied face detection algorithms. 
     A normalization scale for a candidate window can be determined by dividing the luminance&#39;s dynamic range to the luminance range (largest luminance value minus smallest luminance value) within the candidate window. The normalization scale is greater than but about equal to 1 when the luminance range within the candidate window is about equal to the luminance&#39;s dynamic range. A normalization scale determined to be close to 1 can contribute minimal contrast enhancement of the analyzed region associated with the candidate window. In such instance, the benefit for the subsequent classifier-based facial detection process arising from performing the normalization of the luminance variance within the candidate window may be small. Often, the normalization scale can be significantly greater than 1 when the luminance range within the candidate window is smaller than the luminance&#39;s dynamic range. The greater (with respect to 1) the determined normalization scale is, the more significant the contrast enhancement of the analyzed region corresponding to the candidate window may be. 
     The luminance range within a candidate window can be very large, e.g. close to the luminance&#39;s dynamic range, because an often small fraction of the pixels within the candidate window have luminance values that are very different from luminance values of the other pixels of the candidate window. In the example illustrated in  FIG. 1A , this small fraction of pixels can represent pixels from the brightly lit background  40 , while the other pixels of the candidate window can represent pixels from the poorly lit human subject  20 . Procedure  100  is configured to combine the methods described below in connection with  FIG. 1B  for eliminating the small fraction of pixels that skew the luminance range within the candidate window, and for normalizing the luminance variance within an inset region of the candidate window. A smaller luminance range of the inset region determined in accordance with the methods described below in  FIGS. 2A-2C  can lead to a normalization scale significantly greater than 1, which may in turn significantly enhance the contrast of the analyzed image region corresponding to the candidate window. 
       FIG. 1B  is a flow chart of an example method  110  for detecting a portion of a face depicted in an image by using a candidate window which has an inset region for normalizing corresponding luminance variance. In some implementations, method  110  can be performed by data processing apparatus and can be implemented as part of the process  100  illustrated in connection with  FIG. 1A . 
     The method  110  includes establishing  120  a target region of an image that potentially contains at least a portion of a face. The target region can correspond to a candidate window of a face detection procedure, such as the Viola Jones algorithm. In some implementations, the target region can be a rectangle. In some implementations, the rectangular target region representing the candidate window can be a square. Further, the candidate window can be established  120  to have a corresponding size and can be associated with a location along a corresponding analysis path for a given stage of the Viola Jones algorithm. Examples of target regions representing candidate windows established at several locations along an analysis path are illustrated below in connection with  FIGS. 2A-2C . 
     The method  110  also includes establishing  130  an inset region inside the established target region. The inset region is sized to include a predetermined fraction of the target region. In the example illustrated in  FIG. 1A , the predetermined fraction of the target region of the image included in the inset region can be about 85%. 
     In some implementations, the inset region corresponds to a convex portion of the target region of the image such that the convex portion has a size equal to the predetermined fraction of the target region. The convex portion of the target image can be a convex polygon, i.e., a polygon for which all internal angles are less than or equal to 180°. In addition, a convex polygon represents, by definition, a contiguous region. The rectangle and the square are examples of convex polygons. In the example illustrated in  FIG. 1A , the inset region of a square candidate window can be established  130  to be a square having an area that is about 85% of the area of the square candidate window. 
     In some other implementations, the inset region corresponds to a concave (non-convex,) however, contiguous portion of the target region of the image such that the concave portion has a size equal to the predetermined fraction of the target region. 
     In some further implementations, the inset region corresponds to non-contiguous portions of the target region of the image having a cumulative size equal the predetermined fraction of the target region, such that the pixels within the target region excluded from the inset region corresponds to outlier values of the luminance. In the example illustrated in  FIG. 1A , the inset region of a square candidate window can be established  130  to have an area that is about 85% of the area of the square candidate window and to exclude 15% of the pixels within the square candidate window corresponding to the largest values of luminance within the square candidate window. In this fashion, the brightest pixels corresponding to 15% of the total pixels within the candidate window can be ignored when calculating a normalization scale. 
     As another example corresponding to a dark background and a brightly lit face, the inset region of a square candidate window can be established  130  to have an area that is about 85% of the area of the square candidate window and to exclude 15% of the pixels within the square candidate window corresponding to the smallest values of luminance within the square candidate window. In this manner, the darkest pixels corresponding to 15% of the total pixels within the candidate window can be ignored when calculating a normalization scale. In yet another example, it may be desired to calculate a normalization scale by ignoring the darkest and the brightest pixels within a candidate window. Consequently, the inset region of the square candidate window can be established  130  to have an area that is about 85% of the area of the square candidate window, to exclude 7.5% of the pixels within the square candidate window corresponding to the smallest values of luminance within the square candidate window, and to further exclude 7.5% of the pixels within the square candidate window corresponding to the largest values of luminance within the square candidate window. 
     Further, the method  110  includes normalizing  140  a luminance variance of the inset region to obtain a normalization scale. As described below in connection with  FIGS. 2A-2C , normalizing  140  the luminance within the inset region can include identifying pixels of the target region that are outside the established inset region, and obtaining a normalization scale corresponding to the inset region by dividing the luminance&#39;s dynamic range to a luminance range within the inset region. 
     Furthermore, the method  110  includes normalizing  150  a luminance variance of the target region of the image based on the obtained normalization scale corresponding to the inset region. In an example described below in connection with  FIG. 2A , when the obtained normalization scale corresponding to the inset region is larger than a normalization scale corresponding to the target region, normalizing  150  the luminance variance within the target region based on the normalization scale corresponding to the inset region can result in improved contrast for portions of a face contained in the target region relative to contrast obtained from normalizing the luminance variance within the target region based on the normalization scale corresponding to the target region. As another example described below in connection with  FIGS. 2B-2C , when the obtained normalization scale corresponding to the inset region is substantially equal to the normalization scale corresponding to the target region, normalizing  150  the luminance variance within the target region based on the normalization scale corresponding to the inset region can result in contrast for portions of a face contained in the target region that is similar to contrast obtained from normalizing the luminance variance within the target region based on the normalization scale corresponding to the target region. 
     In addition, the method  110  includes detecting  160  a face within the established target region of the image using the established inset region. A classifier-based facial detection process, e.g. the Viola Jones algorithm, can be applied for detecting  160  portions of a face within a candidate window after having normalized the luminance variance of the candidate window in accordance with the steps  130 ,  140  and  150  described above. 
       FIGS. 2A ,  2 B and  2 C show aspects of an example method  200  for normalizing a luminance variance inside a candidate window using a normalization scale obtained by normalizing a luminance variance within an inset region of the candidate window. In some implementations, method  200  can be performed by data processing apparatus and can be combined with the process  100  and/or with method  110  described above in connection with  FIGS. 1A-1B . 
       FIG. 2A  illustrates an instance  10 - 2  of the digital image  10 . A portion of an exemplary analysis path “P” is depicted as a continuous-line serpentine overlaid on the instance  10 - 2  of the digital image  10 . Analysis along a serpentine-shaped path can be performed such that successive scan-lines are processed from left-to-right and right-to-left, respectively. However, the analysis path “P” can have multiple other shapes. For example, the analysis path “P” can be shaped as a raster path. Analysis along a raster-shaped path can be performed such that each scan-line is processed left-to-right, from the top to the bottom of the digital image  10 . 
     Additionally, a candidate window  60  is applied to the instance  10 - 2  of the digital image  10  at a location “2” along the analysis path “P”. The candidate window  60  is depicted as a dashed-line square. Moreover, the candidate window  60  placed at location “2” of analysis path “P” can correspond to an intermediate stage of the method  110  corresponding to establishing  120  of a target region  60  of the instance  10 - 2  of the digital image  10  that potentially contains at least a portion of a face. 
     A set of pixels  28  within the candidate window  60  has low luminance values and potentially corresponds to a portion of a back-lit face. Another set of pixels  44  within the candidate window  60  has high luminance values and corresponds to a bright background. At the location “2” along the analysis path “P”, the set of pixels  44  represents a small fraction of the total pixels of the candidate window  60 . 
     The method  200  includes identifying  230  pixels of the candidate window  60  outside of a specified portion of the candidate window  60 . In accordance with the stage  130  of method  110 , the specified portion of the candidate window  60  has a predetermined size. For example, the size of the specified portion of the candidate window is about 85% of the candidate window  60 . Therefore, the specified portion of the candidate window  60  excludes a preset fraction “f” of the total pixels of the candidate window  60 . For example, the specified portion of the candidate window  60  excludes about 15% of the total pixels of the candidate window  60 . 
     The histogram  231  represents the distribution of luminance among pixels of the candidate window  60 . The x-axis corresponds to the luminance&#39;s dynamic range and has 0 to N values, corresponding to the grey-levels used to represent luminance for a pixel of the candidate window  60 . For example, N=511 if the luminance&#39;s dynamic range is 512 grey-levels. Luminance-values close to 0 correspond to black (dark) pixels of the candidate window; luminance values close to N correspond to white (bright) pixels of the candidate window. The y-axis corresponds to the number of pixels of the candidate window having luminosity values corresponding to each of the histogram bins. The total number of pixels of the candidate window  60  corresponds to the sum of all the bins of the histogram  231 . 
     The histogram  231  shows that the set of pixels  28  corresponding to the dark pixels within the candidate window  60  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range, and that the set of pixels  44  corresponding to the bright pixels within the candidate window  60  is distributed over luminance levels grouped in a narrow interval at the high-end portion the luminance&#39;s dynamic range. Note that the histogram  231  also shows that, in this example, there is a well-defined luminance gap between the sets of pixels  28  and  44  corresponding to the dark and bright pixels, respectively, within the candidate window  60 . Also note that, in this example, the luminance range R1 corresponding to the candidate window  60  is about equal to the luminance&#39;s dynamic range. Accordingly, a normalization scale corresponding to the candidate window  60  defined as the luminance&#39;s dynamic range divided by the luminance range R1 is about equal to 1. 
     Further, the histogram  231  illustrates that the number of pixels in the set  44  corresponds to a fraction “f” of the total number of pixels in the candidate window  60 . In this example, the fraction “f” is about 15%. 
     In some implementations, the portion within the candidate window  60  for calculating the normalization scale can be specified to exclude a preset fraction of pixels of the candidate window  60  such that the specified portion has a convex shape. An example of the specified portion  62  is depicted as a dotted-line square within the candidate window  60 . Note that the size of the inset square  62  represents about 85% of the candidate window  60  and that the inset square  62  has no common pixels with the set of pixels  44  corresponding to the bright pixels of candidate window  60 . Accordingly, the set of pixels  44  can be identified  230  as pixels of the candidate window  60  that are outside the inset square  62 . Note that the bins corresponding to pixels of the candidate window  60  identified  230  to be outside of the specified portion  62  are depicted as cross-hashed bins. Additionally, the fraction “f” of the total pixels of the candidate window  60  that is excluded from the specified portion  62  is encompassed by a dotted circle. 
     In some other implementations, the portion within the candidate window  60  for calculating the normalization scale can be specified to exclude a preset fraction of pixels of the candidate window  60  such that the excluded pixels are luminance outliers. In the example illustrated in  FIG. 2A , the fraction of pixels of the candidate window  60  to be excluded from the specified portion is f=15%. Additionally, the set of pixels  44  constitutes luminance outliers and represents a fraction f=15% of the total number of pixels of the candidate window  60 . Accordingly, the set of pixels  44  can be identified  230  as pixels of the candidate window  60  that are outside the inset square  62 . 
     Method  200  includes determining  240  a luminance range R2 corresponding to the specified portion of the candidate window  60 . The histogram  241  represents the distribution of luminance among pixels of the specified portion of the candidate window  60 . The total number of pixels of the specified portion of the candidate window  60  corresponds to the sum of all the bins of the histogram  241 . 
     The histogram  241  shows that the set of pixels  28  corresponding to the dark pixels within the candidate window  60  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range. Since the specified portion has been established to exclude a preset fraction “f” of pixels associated, in this example, to the set of pixels  44  corresponding to the bright pixels within the candidate window  60 , no luminosity levels are present in the histogram  241  above the highest luminance level-k of the set of pixels  28  corresponding to the dark pixels within the candidate window  60 . Note that, in this example, the luminance range R2 corresponding to the specified portion of the candidate window  60  is equal to the luminance range R2=k. Accordingly, a normalization scale corresponding to the specified portion of the candidate window  60  defined as the luminance&#39;s dynamic range divided by the luminance range R2 is equal to N/k&gt;R1=1. 
     After performing method  200 , the luminance variance of the candidate window  60  can be normalized  150  in accordance with the method  110 . For example, normalizing  150  a luminance variance of the candidate window  60  based on the obtained normalization scale, N/k&gt;1, corresponding to the specified portion of the candidate window  60  leads to improved contrast for portions of a face  52  contained in the candidate window  60  (as shown in instance  10 ″- 2  of the digital image  10 ,) in comparison to contrast obtained from normalizing the luminance variance within the candidate window  60  based on the normalization scale corresponding to the candidate window  60  (as shown in instance  10 - 2  of the digital image  10 .) 
     As described above, in this example, either way of specifying the portion of the candidate window  60 , whether as (i) an inset square  62  having a predetermined size or as (ii) excluding luminance outliers and having a predetermined size, leads to similar improvement in contrast for the face  52  contained in the candidate window  60 . 
     Multiple sizes and relative positions of the inset square  62  relative to the candidate window  60  have been tested. Detailed test results are described below in connection with  FIG. 3 . An exemplary finding of the test result is that the inset square  62  can be sized and positioned relative to the candidate window  60  such that, when the candidate window  60  inscribes a face, the inset square  62  is (i) sufficiently large to include at least eyes, a nose and a mouth of the inscribed face, and is (ii) sufficiently small to exclude portions of the candidate window  60  that are not part of the inscribed face. 
       FIG. 2B  illustrates an instance  10 - 1  of the digital image  10 . A portion of the exemplary analysis path “P” is depicted as a continuous-line serpentine overlaid on the instance  10 - 1  of the digital image  10 . Additionally, a candidate window  60  is applied to the instance  10 - 1  of the digital image  10  at a location “1” along the analysis path “P”. The candidate window  60  is depicted as a dashed-line square. Moreover, the candidate window  60  placed at location “1” of analysis path “P” can correspond to an intermediate stage of the method  110  corresponding to establishing  120  of a target region  60  of the instance  10 - 1  of the digital image  10  that potentially contains at least a portion of a face. A portion within the candidate window  60  for calculating the normalization scale can be specified to exclude a preset fraction of pixels of the candidate window  60  such that the specified portion has a convex shape. An example of the specified portion  62  is depicted as a dotted-line square within the candidate window  60 . 
     A set of pixels  26  within the candidate window  60  has low luminance values and potentially corresponds to a portion of a back-lit face. Another set of pixels  42  within the candidate window  60  has high luminance values and corresponds to a bright background. At the location “1” along the analysis path “P”, the sets of pixels  26  and  42  are approximately equal in size. 
     An instance  200 # of the method  200  includes identifying  232  pixels of the candidate window  60  outside of an inset square  62  of the candidate window  60 . In accordance with stage  130  of the method  110 , the inset square  62  of the candidate window  60  has a predetermined size. For example, the size of the inset square  62  is about 85% of the candidate window  60 . Therefore, the inset square  62  excludes a preset fraction “f” of the total pixels of the candidate window  60 . For example, the inset square  62  excludes about 15% of the total pixels of the candidate window  60 . 
     The histogram  233  represents the distribution of luminance among pixels of the candidate window  60 . The total number of pixels of the candidate window  60  corresponds to the sum of all the bins of the histogram  233 . The histogram  233  shows that the set of pixels  26  corresponding to the dark pixels within the candidate window  60  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range, and that the set of pixels  42  corresponding to the very bright pixels within the candidate window  60  is distributed over luminance levels grouped in a narrow interval at the high-end portion the luminance&#39;s dynamic range. Note that the histogram  233  also shows that, in this example, there is a well-defined luminance gap between the sets of pixels  26  and  42  corresponding to the dark and bright pixels, respectively, within the candidate window  60 . The number of pixels of set  26  corresponding to the dark pixels within the candidate window  60  is about equal to the number of pixels of set  42  corresponding to the bright pixels within the candidate window  60 . 
     Also note that, in this example, the luminance range R1 corresponding to the candidate window  60  is about equal to the luminance&#39;s dynamic range. Accordingly, a normalization scale corresponding to the candidate window  60  defined as the luminance&#39;s dynamic range divided by the luminance range R1 is about equal to 1. 
     The bins corresponding to the preset fraction “f” of pixels of the candidate window  60  identified  232  to be outside of the inset square  62  are depicted as cross-hashed portions of histogram bins. In addition, the fraction “f” of the total pixels of the candidate window  60  that are excluded from the inset square  62  is encompassed by dotted ellipses. As illustrated in the instance  10 - 2  of digital image  10 , the pixels identified  232  to be outside of the inset square  62  include about equal numbers of bright pixels corresponding to the set of pixels  42  and dark pixels corresponding to the set of pixels  26 . This is reflected in histogram  233  where the cross-hashed portions of histogram bins are substantially uniformly distributed among the bins of histogram  233 . 
     The instance  200 # of method  200  includes determining  242  a luminance range R2 corresponding to the inset square  62  of the candidate window  60 . The histogram  243  represents the distribution of luminance among pixels of the inset square  62  of the candidate window  60 . The total number of pixels of the inset square  62  of the candidate window  60  correspond to the sum of all the bins of the histogram  243 . 
     The histogram  243  shows that the set of pixels  26 # corresponding to the dark pixels within the inset square  62  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range, and that the set of pixels  42 # corresponding to the bright pixels within the inset square  62  is distributed over luminance levels grouped in a narrow interval at the high-end portion the luminance&#39;s dynamic range. As in the histogram  233 , note the presence of a well-defined luminance gap between the sets of pixels  26 # and  42 # corresponding to the dark and bright pixels, respectively, within the inset square  62 . Once again, the number of pixels of set  26 # corresponding to the dark pixels within the inset square  62  is about equal to the number of pixels of set  42 # corresponding to the bright pixels within the inset square  62 . 
     Also note that, in this example, the luminance range R2 corresponding to the inset square  62  is about equal to the luminance&#39;s dynamic range. Accordingly, a normalization scale corresponding to the inset square  62  defined as the luminance&#39;s dynamic range divided by the luminance range R2 is about equal to 1. Therefore, whether obtained within the entire candidate window  60  or only within an inset square  62 , the respective normalization ranges are about equal to each other and equal to 1, R1=R2=1. 
     After performing the instance  200 # of method  200 , the luminance variance of the candidate window  60  can be normalized  150  in accordance with the method  110 . For example, normalizing  150  a luminance variance of the candidate window  60  based on the obtained normalization scale corresponding to the inset square  62  (as shown in instance  10 ″- 1  of the digital image  10 ) can lead to contrast for portions of a face contained in candidate window  60  that is similar to contrast obtained from normalizing the luminance variance within candidate window  60  based on the normalization scale corresponding to the candidate window  60  (as shown in instance  10 - 1  of the digital image  10 .) 
       FIG. 2C  illustrates an instance  10 - 1  of the digital image  10 . A portion of the exemplary analysis path “P” is depicted as a continuous-line serpentine overlaid on the instance  10 - 1  of the digital image  10 . Additionally, a candidate window  60  is applied to the instance  10 - 1  of the digital image  10  at a location “1” along the analysis path “P”. The candidate window  60  is depicted as a dashed-line square. Moreover, the candidate window  60  placed at location “1” of analysis path “P” can correspond to an intermediate stage of the method  110  corresponding to establishing  120  of a target region  60  of the instance  10 - 1  of the digital image  10  that potentially contains at least a portion of a face. A portion within the candidate window  60  for calculating the normalization scale can be specified to exclude a preset fraction of pixels of the candidate window  60  such that the excluded pixels are luminance outliers. 
     A set of pixels  26  within the candidate window  60  has low luminance values and potentially corresponds to a portion of a back-lit face. Another set of pixels  42  within the candidate window  60  has high luminance values and corresponds to a bright background. At the location “1” along the analysis path “P”, the sets of pixels  26  and  42  are approximately equal in size. 
     An instance  200 * of method  200  includes identifying  234  pixels of the candidate window  60  outside of a specified portion of the candidate window  60 . In accordance with stage  130  of the method  110 , the specified portion of the candidate window  60  has a predetermined size. For example, the size of the specified portion is about 85% of the candidate window  60 . Therefore, the specified portion of the candidate window  60  excludes a preset fraction “f” of the total pixels of the candidate window  60 . For example, the specified portion of the candidate window  60  excludes about 15% of the total pixels of the candidate window  60 . 
     The histogram  235  represents the distribution of luminance among pixels of the candidate window  60 . The total number of pixels of the candidate window  60  corresponds to the sum of all the bins of the histogram  235 . The histogram  235  shows that the set of pixels  26  corresponding to the dark pixels within the candidate window  60  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range, and that the set of pixels  42  corresponding to the very bright pixels within the candidate window  60  is distributed over luminance levels grouped in a narrow interval at the high-end portion the luminance&#39;s dynamic range. Note that the histogram  235  also shows that, in this example, there is a well-defined luminance gap between the sets of pixels  26  and  42  corresponding to the dark and bright pixels, respectively, within the candidate window  60 . The number of pixels of set  26  corresponding to the dark pixels within the candidate window  60  is about equal to the number of pixels of set  42  corresponding to the very bright pixels within the candidate window  60 . 
     Also note that, in this example, the luminance range R1 corresponding to the candidate window  60  is about equal to the luminance&#39;s dynamic range. Accordingly, a normalization scale corresponding to the candidate window  60  defined as the luminance&#39;s dynamic range divided by the luminance range R1 is about equal to 1. 
     The bins corresponding to the preset fraction “f” of pixels of the candidate window  60  identified  232  to be outside of the specified portion of the candidate window  60  are depicted as cross-hashed portions of histogram bins. In addition, the fraction “f” of the total pixels of the candidate window  60  that is excluded from the specified portion of the candidate window  60  is encompassed by a dotted ellipse. As illustrated in the histogram  235 , the pixels identified  234  to be outside of the specified portion of the candidate window  60  include outliers from among the set of pixels  42  corresponding to the bright pixels within the candidate window  60 . Accordingly, the pixels identified  234  to be outside of the specified portion of the candidate window  60  are selected from the bin corresponding to the highest luminance value of the set of pixels  42 . 
     The instance  200 * of method  200  includes determining  244  a luminance range R2 corresponding to the specified portion of the candidate window  60 . The histogram  245  represents the distribution of luminance among pixels of the specified portion of the candidate window  60 . The total number of pixels of the specified portion of the candidate window  60  corresponds to the sum of all the bins of the histogram  245 . 
     The histogram  245  shows that the set of pixels  26  corresponding to the dark pixels within the specified portion of the candidate window  60  is distributed over luminance levels in the lower-third portion the luminance&#39;s dynamic range, and that the set of pixels  42 * corresponding to the bright pixels within the specified portion of the candidate window  60  is distributed over luminance levels grouped in a narrow interval at the high-end portion the luminance&#39;s dynamic range. As in the histogram  235 , note the presence of the luminance gap between the sets of pixels  26  and  42 * corresponding to the dark and bright pixels, respectively, within the specified portion of the candidate window  60  is preserved unchanged. Once again, the number of pixels of set  26  corresponding to the dark pixels within the inset square  62  is about equal to the number of pixels of set  42 * corresponding to the bright pixels within the inset square  62 . 
     Also note that, in this example, the luminance range R2 corresponding to the specified portion of the candidate window  60  is about equal to the luminance&#39;s dynamic range. Accordingly, a normalization scale corresponding to the specified portion of the candidate window  60  defined as the luminance&#39;s dynamic range divided by the luminance range R2 is about equal to 1. Therefore, whether obtained within the entire candidate window  60  or only within a specified portion of the candidate window  60 , the respective normalization ranges are about equal to each other and equal to 1, R1=R2=1. 
     After performing the instance  200 * of method  200 , the luminance variance of the candidate window  60  can be normalized  150  in accordance with the method  110 . For example, normalizing  150  a luminance variance of the candidate window  60  based on the obtained normalization scale corresponding to the specified portion of the candidate window  60  (as shown in instance  10 ″- 1  of the digital image  10 ) can lead to contrast for portions of a face contained in candidate window  60  that is similar to contrast obtained from normalizing the luminance variance within candidate window  60  based on the normalization scale corresponding to the candidate window  60  (as shown in instance  10 - 1  of the digital image  10 .) 
       FIG. 3  shows experimental results obtained based on methods  100 ,  110  and  200  described in this specification. A candidate window  60  can be a rectangle having sizes Lx and Ly. An example of the candidate window  60  is depicted in  FIG. 3  as a dashed-line rectangle. In some implementations, the candidate window  60  can be a square, Lx=Ly. A region within the candidate window  60  for calculating the normalization scale can be specified to exclude a preset fraction of pixels of the candidate window  60  such that the specified region has a convex shape. An example of the specified region  62  is depicted in  FIG. 3  as a dotted-line rectangle within the candidate window  60 . An inset value Δx can be used to specify a reduction in the size of Lx for the candidate window  60  corresponding to the inset rectangle  62 . Similarly, an inset value Δy can be used to specify a reduction in the size of Ly for the candidate window  60  corresponding to the inset rectangle. In some implementations, the inset values Δx and Δy are the same: Δx=Δy. 
     Panel (a) shows a graph  310  depicting test results for determining correctly detected faces vs. a fraction Δx/L (%). Panel (b) shows a graph  320  depicting test results for determining false positives vs. the fraction Δx/L (%). The tests were carried out for square candidate windows, Lx=Ly, and for equal insets corresponding to Lx and Ly: Δx=Δy. The x-axis of graphs  310  and  320  can be used to determine an area of the inset rectangle  62  relative to the area of the candidate window  60 . For x/Lx=0, the inset square  62  represents 100% of the area of the candidate window  60 , i.e., no inset square was used. 
     For example, for x/Lx=2%, the inset square  62  represents 92% of the area of the candidate window  60 . For inset squares  62  having an area less than 92% of the candidate window  60 , an increase in the number of correctly detected faces can be observed. As another example, for x/Lx=4%, the inset square  62  represents 85% of the area of the candidate window  60 . For inset squares  62  having an area less than 85% of the candidate window  60 , the increase in the number of correctly detected faces levels off, while the number of false positives continues to increase. As yet another example, for x/Lx=5%, the inset square  62  represents 81% of the area of the candidate window  60 . For inset squares  62  having an area less than 81% of the candidate window  60 , the increase in the number of correctly detected faces and the increase in the number of false positives have reached saturation. 
     The tests have been carried out on 24,600 images. When an inset square  62  is used, an adjusted face detection algorithm based on methods  100 ,  110  and  200  enables detection of ˜1100 faces more than when not using the inset square  62  (out of ˜24600 faces), while increasing the false positives by ˜85. Different inset values Δx/L (%) can be used depending what trade-off between false negatives and false positives is desired. Based on experimentation, a 92.2% of the candidate window  60  was chosen (inset all sides by 3.91%). That value was chosen for computational elegance: 5/128 is efficient to compute in fixed-point arithmetic, and results in a 4.4% improvement in detection rate. 
     Based on these results, in some implementations a predetermined fraction of the candidate window  60  and the inset square  62  can be larger than 80%. In some implementations, the predetermined fraction of the candidate window  60  included in the inset square  62  can be less than 95%. In some implementations, the predetermined fraction of the candidate window  60  included in the inset square  62  is about 92%. 
       FIG. 4  is a flow chart of an exemplary method  400  for detecting a portion of a face depicted in a color image by biasing the image&#39;s luminance data towards skin-tone prior to using candidate windows. In some implementations, method  400  can be performed by data processing apparatus and can be combined with the process  100  and/or with methods  110  and  200  described above in this specification. 
     The method  400  includes establishing  410  a combination of color space component values weighted to generate skin-tone bias. For example, a linear combination alpha*Y+(1-alpha)*Cr can be used for images in YCbCr color-space. Alpha=1 corresponds to the unmodified YCbCr grey-levels. Red-bias increases as alpha decreases from 1 to 0. For example, it was determined experimentally that alpha=0.6 corresponds to skin-tone bias and produces the best test results, as described below in connection with  FIG. 5 . In addition, cyan-bias increases as alpha increases larger than 1. 
     The method  400  further includes converting color  420  data associated with an image to skin-tone-biased luminance data in accordance with the established combination. By performing a particular conversion of image data from color to grey-scale to bias the image&#39;s luminance towards skin-tone, the contrast of face-specific features can be selectively pre-enhanced, even before normalizing the luminance variance. 
     Also, the method  400  includes establishing  430  a target region of the image that potentially contains at least a portion of a face. The target region can correspond to facial detection algorithms that are known in the art, such as the Viola Jones, Rowley Baluja Kanade, or Schneiderman Kanade algorithms. In some implementations, the target region can be a square. Further, the candidate window can be established to have a corresponding size and can be associated with a location along a corresponding analysis path for a given stage of the facial detection algorithm. Examples of target regions representing candidate windows established at several locations along an analysis path are illustrated above in connection with  FIGS. 2A-2C . 
     Further, the method  400  includes normalizing  440  a variance of the converted skin-tone-biased luminance data associated with the target region of the image. Normalizing  440  the converted skin-tone-biased luminance variance within the target region can include obtaining a normalization scale dividing the luminance&#39;s dynamic range to a luminance range within the target region. 
     Furthermore, the method  400  includes detecting  450  a face within the target region of the image after said normalizing. A classifier-based facial detection process, e.g. the Viola Jones algorithm, can be applied for detecting  450  portions of a face within a candidate window after having normalized  440  the converted skin-tone-biased luminance variance of the candidate window. 
     Optionally, the method  400  can include establishing  434  an inset region inside the established target region. The inset region is sized to include a predetermined fraction of the target region. Establishing  434  the inset region inside the target region can be performed as described above in accordance with aspect  130  of method  110 , and with method  200 . 
     The method  400  can further include, optionally, normalizing  438  a luminance variance of the converted skin-tone-biased data of the inset region to obtain a normalization scale. Normalizing  438  the luminance variance of the converted skin-tone-biased data of the inset region can be performed as described above with respect to aspect  140  of method  110 , and with method  200 . Normalizing  440  the variance of the converted skin-tone-biased luminance data associated with the target region of the image can be optionally performed  444  in accordance with the obtained normalization scale. 
       FIG. 5  shows additional experimental results obtained based on the methods  110 ,  200  and  400  described in this specification. Graph  510  represents test results for quantifying false negatives (depicted as triangles) and false positive (depicted as circles) in regards with face detection as a function of alpha—a measure of skin-tone-bias applied to image data prior to performing facial detection procedures. 
     Each of the over 24,000 test images was first converted in YCbCr space, where Y is the luminance, and (Cr, Cb) is the chrominance. Moreover, instead of using the Y channel for feature detection, a linear combination alpha*Y+(1-alpha)*Cr was used. For example, alpha=1 corresponds to the regular YCbCr space. Values of alpha greater than 1 provide cyan-bias, while values of alpha less than 1 provide red-bias to the luminance component Y. Facial detection techniques were performed on the test images after applying the foregoing color-space conversion. Normalization of the converted skin-tone-biased luminance variance within candidate windows used by the face detection algorithms was performed based on normalization scales corresponding to inset windows of the candidate windows, in accordance with methods  110  and  200 . 
     Improvement in face detection rate was obtained when the linear combination used to bias the luminance data used a value of alpha=0.6. For this value of alpha, approximate 40 more faces were detected and approximate 40 less false positives were noted, when compared with alpha=1. Parameter alpha represents a measure of skin-tone-bias of the luminance conversion, and both the plot of false negatives  512  and the plot of false positives  514  have minima at alpha=0.6. Further, by selecting values of alpha between 0.5 and 0.7 the contrast of face-specific features can be pre-enhanced prior to normalizing the luminance variance. 
     A multitude of computing devices may be used to implement the systems and methods described in this document, as either a client or as a server or plurality of servers. A computing device can be implemented in various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Another computing device can be implemented in various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, and other similar computing devices. Additionally, computing devices can include Universal Serial Bus (USB) flash drives. The USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device. The components described here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document. 
     A computing device can include a processor, memory, a storage device, a high-speed interface connecting to memory and high-speed expansion ports. The computing device can further include a low speed interface connecting to a low speed bus and a storage device. Each of the above components can be interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the computing device, including instructions stored in the memory or on the storage device to display graphical information for a GUI on an external input/output device, such as a display coupled to high speed interface. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). 
     The memory can store information within the computing device. In one implementation, the memory can be a volatile memory unit or units. In another implementation, the memory can be a non-volatile memory unit or units. The memory may also be another form of computer-readable medium, such as a magnetic or optical disk. 
     The storage device can provide mass storage for the computing device. In one implementation, the storage device may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly implemented in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory, the storage device, or memory on processor. 
     The high speed controller can manage bandwidth-intensive operations for the computing device, while the low speed controller can manage lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller can be coupled to memory, to a display (e.g., through a graphics processor or accelerator), and to high-speed expansion ports, which may accept various expansion cards. In the implementation, low-speed controller can be coupled to the storage device and the low-speed expansion port. The low-speed expansion port, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter. 
     The computing device may be implemented in a number of different forms. For example, it may be implemented as a standard server, or multiple times in a group of such servers. It may also be implemented as part of a rack server system. In addition, it may be implemented in a personal computer such as a laptop computer. Alternatively, components from computing device may be combined with other components in a mobile device. Each of such devices may contain one or more computing devices or mobile devices, and an entire system may be made up of multiple computing devices and mobile devices communicating with each other. 
     A mobile device can include a processor, memory, an input/output device such as a display, a communication interface, and a transceiver, among other components. The mobile device may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the above components is interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate. 
     The processor can execute instructions within the mobile device, including instructions stored in the memory. The processor of the mobile device may be implemented as a chipset of chips that include separate and multiple analog and digital processors. Additionally, the processor may be implemented using any of a number of architectures. For example, the processor may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor. The processor may provide, for example, for coordination of the other components of the mobile device, such as control of user interfaces, applications run by the mobile device, and wireless communication by the mobile device. 
     The processor of the mobile device may communicate with a user through control interface and display interface coupled to a display. The display may be, for example, a Thin-Film-Transistor Liquid Crystal display or an Organic Light Emitting Diode display, or other appropriate display technology. The display interface may include appropriate circuitry for driving the display to present graphical and other information to a user. The control interface may receive commands from a user and convert them for submission to the processor of the mobile device. In addition, an external interface may provide in communication with processor of the mobile device, so as to enable near area communication of the mobile device with other devices. The external interface may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used. 
     The memory stores information within the computing mobile device. The memory can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. An expansion memory may also be provided and connected to the mobile device through an expansion interface, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory may provide extra storage space for the mobile device, or may also store applications or other information for the mobile device. Specifically, expansion memory may include instructions to carry out or supplement the processes described above, and may include secure information also. Thus, for example, expansion memory may be provide as a security module for the mobile device, and may be programmed with instructions that permit secure use of device. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner. 
     The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly implemented in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory, expansion memory, or memory on processor that may be received, for example, over transceiver or external interface. 
     The mobile device may communicate wirelessly through communication interface, which may include digital signal processing circuitry where necessary. Communication interface may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through a radio-frequency transceiver. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module may provide additional navigation- and location-related wireless data to the mobile device, which may be used as appropriate by applications running on the mobile device. 
     The mobile device may also communicate audibly using audio codec, which may receive spoken information from a user and convert it to usable digital information. Audio codec may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of the mobile device. The sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on the mobile device. 
     The mobile computing device may be implemented in a number of different forms. For example, it may be implemented as a cellular telephone. It may also be implemented as part of a smartphone, personal digital assistant, or other similar mobile device. 
     Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. 
     These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” “computer-readable medium” refers to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor. 
     To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input. 
     The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), peer-to-peer networks (having ad-hoc or static members), grid computing infrastructures, and the Internet. 
     The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. 
     While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any inventions or of what may be claimed, but rather as descriptions of features specific to particular implementations of particular inventions. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described above should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. 
     Thus, particular implementations of the subject matter have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous.

Metadata:
Filing Date: 20101026
Publication Date: 20140422
Grant Date: 20140422
Priority Date: 20101026
Inventors: BRUNNER RALPH
Assignee: APPLE INC
CPC Classifications: [{"code": "G06V40/162", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06V40/162", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N9/643", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N5/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N9/643", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T2207/30201", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N5/20", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/94", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T5/94", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 50481894