Patent Publication Number: US-2007098303-A1

Title: Determining a particular person from a collection

Description:
FIELD OF THE INVENTION  
      The invention relates generally to the field of image processing. More specifically, the invention relates to estimating and correcting for unintentional rotational camera angles that occur at the time of image capture, based upon the captured image&#39;s corresponding location of its vanishing points. Furthermore, the invention relates to performing such image processing in a digital camera.  
      The present invention relates to determining if objects or persons of interest are in particular images of a collection of digital images.  
     BACKGROUND OF THE INVENTION  
      With the advent of digital photography, consumers are amassing large collections of digital images and videos. The average number of images captures with digital cameras per photographer is still increasing each year. As a consequence, the organization and retrieval of images and videos is already a problem for the typical consumer. Currently, the length of time spanned by a typical consumer&#39;s digital image collection is only a few years. The organization and retrieval problem will continue to grow as the length of time spanned by the average digital image and video collection increases.  
      A user desires to find images and videos containing a particular person of interest. The user can perform a manual search to find images and videos containing the person of interest. However this is a slow, laborious process. Even though some commercial software (e.g. Adobe Album) allows users to tag images with labels indicating the people in the images so that searches can later be done, the initial labeling process is still very tedious and time consuming.  
      Face recognition software assumes the existence of a ground-truth labeled set of images (i.e. a set of images with corresponding person identities). Most consumer image collections do not have a similar set of ground truth. In addition, the labeling of faces in images is complex because many consumer images have multiple persons. So simply labeling an image with the identities of the people in the image does not indicate which person in the image is associated with which identity.  
      There exists many image processing packages that attempt to recognize people for security or other purposes. Some examples are the FaceVACS face recognition software from Cognitec Systems GmbH and the Facial Recognition SDKs from Imagis Technologies Inc. and Identix Inc. These packages are primarily intended for security-type applications where the person faces the camera under uniform illumination, frontal pose and neutral expression. These methods are not suited for use in personal consumer images due to the large variations in pose, illumination, expression and face size encountered in images in this domain.  
     SUMMARY OF THE INVENTION  
      It is an object of the present invention to readily identify objects or persons of interests in images or videos in a digital image collection. This object is achieved by a method of identifying a particular person in a digital image collection, wherein at least one of the images in the digital image collection contains more than one person, comprising:  
      (a) providing at least one first label for a first image in the digital image collection containing a particular person and at least one other person; wherein the first label identifies the particular person and a second label for a second image in the digital image collection that identifies the particular person;  
      (b) using the first and second labels to identify the particular person;  
      (c) determining features related to the particular person from the first image or second image or both; and  
      (d) using such particular features to identify another image in the digital image collection believed to contain the particular person.  
      This method has the advantage of allowing users to find persons of interest with an easy to use interface. Further, the method has the advantage that images are automatically labeled with labels related to the person of interest, and allowing the user to review the labels. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The subject matter of the invention is described with reference to the embodiments shown in the drawings.  
       FIG. 1  is a block diagram of a camera phone based imaging system that can implement the present invention;  
       FIG. 2  is a flow chart of an embodiment of the present invention for finding a person of interest in a digital image collection;  
       FIG. 3  is a flow chart of an embodiment of the present invention for finding a person of interest in a digital image collection;  
       FIG. 4  shows a representative set of images used to initiate a search for a person of interest;  
       FIG. 5  shows a representative subset of images displayed to the user as a result of searching for a person of interest;  
       FIG. 6  shows the subset of images displayed to the user after the user has removed images not containing the person of interest;  
       FIG. 7  is a flow chart of an alternative embodiment of the present invention for finding a person of interest in a digital image collection;  
       FIG. 8  shows images and associated labels;  
       FIG. 9  shows a representative subset of images displayed to the user as a result of searching for a person of interest;  
       FIG. 10  shows the subset of images and labels displayed to the user after the user has removed images not containing the person of interest;  
       FIG. 11  shows a more detailed view of the feature extractor from  FIG. 2 ;  
       FIG. 12A  shows a more detailed view of the person detector from  FIG. 2 ;  
       FIG. 12B  is a plot of the relationship of the difference in image capture times and the probability that a person who appeared in one image will also appear in the second image;  
       FIG. 12C  is a plot of the relationship of face size ratio as a function of difference in image capture times;  
       FIG. 12D  is a representation of feature points extracted from a face by the feature extractor of  FIG. 2 ;  
       FIG. 12E  is a representation of face regions, clothing regions, and background regions;  
       FIG. 12F  is a representation of various facial feature regions;  
       FIG. 13  shows a more detailed view of the person finder of  FIG. 2 .  
       FIG. 14  shows a plot of local features for 15 faces, the actual identities of the faces, and the possible identities of the faces; and  
       FIG. 15  is a flow chart of an embodiment of the present invention for finding an object of interest in a digital image collection. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
      In the following description, some embodiments of the present invention will be described as software programs. Those skilled in the art will readily recognize that the equivalent of such a method may also be constructed as hardware or software within the scope of the invention.  
      Because image manipulation algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the method in accordance with the present invention. Other aspects of such algorithms and systems, and hardware or software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein can be selected from such systems, algorithms, components, and elements known in the art. Given the description as set forth in the following specification, all software implementation thereof is conventional and within the ordinary skill in such arts.  
       FIG. 1  is a block diagram of a digital camera phone  301  based imaging system that can implement the present invention. The digital camera phone  301  is one type of digital camera. Preferably, the digital camera phone  301  is a portable battery operated device, small enough to be easily handheld by a user when capturing and reviewing images. The digital camera phone  301  produces digital images that are stored using the image data/memory  330 , which can be, for example, internal Flash EPROM memory, or a removable memory card. Other types of digital image storage media, such as magnetic hard drives, magnetic tape, or optical disks, can alternatively be used to provide the image/data memory  330 .  
      The digital camera phone  301  includes a lens  305  that focuses light from a scene (not shown) onto an image sensor array  314  of a CMOS image sensor  311 . The image sensor array  314  can provide color image information using the well-known Bayer color filter pattern. The image sensor array  314  is controlled by timing generator  312 , which also controls a flash  303  in order to illuminate the scene when the ambient illumination is low. The image sensor array  314  can have, for example, 1280 columns×960 rows of pixels.  
      In some embodiments, the digital camera phone  301  can also store video clips, by summing multiple pixels of the image sensor array  314  together (e.g. summing pixels of the same color within each 4 column×4 row area of the image sensor array  314 ) to create a lower resolution video image frame. The video image frames are read from the image sensor array  314  at regular intervals, for example using a  24  frame per second readout rate.  
      The analog output signals from the image sensor array  314  are amplified and converted to digital data by the analog-to-digital (A/D) converter circuit  316  on the CMOS image sensor  311 . The digital data is stored in a DRAM buffer memory  318  and subsequently processed by a digital processor  320  controlled by the firmware stored in firmware memory  328 , which can be flash EPROM memory. The digital processor  320  includes a real-time clock  324 , which keeps the date and time even when the digital camera phone  301  and digital processor  320  are in their low power state.  
      The processed digital image files are stored in the image/data memory  330 . The image/data memory  330  can also be used to store the user&#39;s personal calendar information, as will be described later in reference to  FIG. 11 . The image/data memory can also store other types of data, such as phone numbers, to-do lists, and the like.  
      In the still image mode, the digital processor  320  performs color interpolation followed by color and tone correction, in order to produce rendered sRGB image data. The digital processor  320  can also provide various image sizes selected by the user. The rendered sRGB image data is then JPEG compressed and stored as a JPEG image file in the image/data memory  330 . The JPEG file uses the so-called “Exif” image format described earlier. This format includes an Exif application segment that stores particular image metadata using various TIFF tags. Separate TIFF tags can be used, for example, to store the date and time the picture was captured, the lens f/number and other camera settings, and to store image captions. In particular, the ImageDescription tag can be used to store labels. The real-time clock  324  provides a capture date/time value, which is stored as date/time metadata in each Exif image file.  
      A location determiner  325  provides the geographic location associated with an image capture. The location is preferably stored in units of latitude and longitude. Note that the location determiner  325  may determine the geographic location at a time slightly different than the image capture time. In that case, the location determiner  325  can use a geographic location from the nearest time as the geographic location associated with the image. Alternatively, the location determiner  325  can interpolate between multiple geographic positions at times before and/or after the image capture time to determine the geographic location associated with the image capture. Interpolation can be necessitated because it is not always possible for the location determiner  325  to determine a geographic location. For example, the GPS receivers often fail to detect signal when indoors. In that case, the last successful geographic location (i.e. prior to entering the building) can be used by the location determiner  325  to estimate the geographic location associated with a particular image capture. The location determiner  325  may use any of a number of methods for determining the location of the image. For example, the geographic location may be determined by receiving communications from the well-known Global Positioning Satellites (GPS).  
      The digital processor  320  also creates a low-resolution “thumbnail” size image, which can be created as described in commonly-assigned U.S. Pat. No. 5,164,831 to Kuchta, et al., the disclosure of which is herein incorporated by reference. The thumbnail image can be stored in RAM memory  322  and supplied to a color display  332 , which can be, for example, an active matrix LCD or organic light emitting diode (OLED). After images are captured, they can be quickly reviewed on the color LCD image display  332  by using the thumbnail image data.  
      The graphical user interface displayed on the color display  332  is controlled by user controls  334 . The user controls  334  can include dedicated push buttons (e.g. a telephone keypad) to dial a phone number, a control to set the mode (e.g. “phone” mode, “camera” mode), a joystick controller that includes 4-way control (up, down, left, right) and a push-button center “OK” switch, or the like.  
      An audio codec  340  connected to the digital processor  320  receives an audio signal from a microphone  342  and provides an audio signal to a speaker  344 . These components can be used both for telephone conversations and to record and playback an audio track, along with a video sequence or still image. The speaker  344  can also be used to inform the user of an incoming phone call. This can be done using a standard ring tone stored in firmware memory  328 , or by using a custom ring-tone downloaded from a mobile phone network  358  and stored in the image/data memory  330 . In addition, a vibration device (not shown) can be used to provide a silent (e.g. non audible) notification of an incoming phone call.  
      A dock interface  362  can be used to connect the digital camera phone  301  to a dock/charger  364 , which is connected to a general control computer  40 . The dock interface  362  may conform to, for example, the well-know USB interface specification. Alternatively, the interface between the digital camera  301  and the general control computer  40  can be a wireless interface, such as the well-known Bluetooth wireless interface or the well-know 802.11b wireless interface. The dock interface  362  can be used to download images from the image/data memory  330  to the general control computer  40 . The dock interface  362  can also be used to transfer calendar information from the general control computer  40  to the image/data memory in the digital camera phone  301 . The dock/charger  364  can also be used to recharge the batteries (not shown) in the digital camera phone  301 .  
      The digital processor  320  is coupled to a wireless modem  350 , which enables the digital camera phone  301  to transmit and receive information via an RF channel  352 . A wireless modem  350  communicates over a radio frequency (e.g. wireless) link with the mobile phone network  358 , such as a 3GSM network. The mobile phone network  358  communicates with a photo service provider  372 , which can store digital images uploaded from the digital camera phone  301 . These images can be accessed via the Internet  370  by other devices, including the general control computer  40 . The mobile phone network  358  also connects to a standard telephone network (not shown) in order to provide normal telephone service.  
      An embodiment of the invention is illustrated in  FIG. 2 . A digital image collection  102  containing people is searched for a person of interest by a person finder  108 . A digital image collection subset  112  is the set of images from the digital image collection  102  believed to contain the person of interest. The digital image collection  102  includes both images and videos. For convenience, the term “image” refers to both single images and videos. Videos are a collection of images with accompanying audio and sometimes text. The digital image collection subset  112  is displayed on the display  332  for review by the human user.  
      The search for a person of interest is initiated by a user as follows: Images or videos of the digital image collection  102  are displayed on the display  332  and viewed by the user. The user establishes one or more labels for one or more of the images with a labeler  104 . A feature extractor  106  extracts features from the digital image collection in association with the label(s) from the labeler  104 . The features are stored in association with labels in a database  114 . A person detector  110  can optionally be used to assist in the labeling and feature extraction. When the digital image collection subset  112  is displayed on the display  332 , the user can review the results and further label the displayed images.  
      A label from the labeler  104  indicates that a particular image or video contains a person of interest and includes at least one of the following:  
      (1) the name of a person of interest in an image or video. A person&#39;s name can be a given name or a nickname.  
      (2) an identifier associated with the person of interest such as a text string or identifier such as “Person A” or “Person B”.  
      (3) the location of the person of interest within the image or video. Preferably, the location of the person of interest is specified by the coordinates (e.g. the pixel address of row and column) of the eyes of the person of interest (and the associated frame number in the case of video). Alternatively, the location of the person of interest can be specified by coordinates of a box that surrounds the body or the face of the person of interest. As a further alternative, the location of the person of interest can be specified by coordinates indicating a position contained within the person of interest. The user can indicate the location of the person of interest by using a mouse to click on the positions of the eyes for example. When the person detector  110  detects a person, the position of the person can be highlighted to the user by, for example, circling the face on the display  332 . Then the user can provide the name or identifier for the highlighted person, thereby associating the position of the person with the user provided label. When more than one person is detected in an image, the positions of the persons can be highlighted in turn and labels can be provided by the user for any of the people.  
      (4) an indication to search for images or videos from the image collection believed to contain the person of interest.  
      (5) the name or identifier of a person of interest who is not in the image.  
      The digital image collection  102  contains at least one image having more than one person. A label is provided by the user via the labeler  104 , indicating that the image contains a person of interest. Features related to the person of interest are determined by the feature extractor  106 , and these features are used by the person finder  108  to identify other images in the collection that are believed to contain the person of interest.  
      Note that the terms “tag”, “caption”, and “annotation” are used synonymously with the term “label.” 
       FIG. 3  is a flow diagram showing a method for using a digital camera to identify images believed to contain a person of interest. Those skilled in the art will recognize that the processing platform for using the present invention can be a camera, a personal computer, a remote computer assessed over a network such as the Internet, a printer, or the like. In this embodiment, a user selects a few images or videos containing a person of interest, and the system determines and displays images or videos from a subset of the digital image collection believed to contain the person of interest. The displayed images can be reviewed by the user, and the user can indicate whether the displayed images do contain the person of interest. In addition , the user can verify or provide the name of the person of interest. Finally, based on the input from the user, the system can again determine a set of images believed to contain the person of interest.  
      In block  202 , images are displayed on the display  332 . In block  204 , the user selects images, where each image contains the person of interest. At least one of the selected images contains a person besides the person of interest. For example,  FIG. 4  shows a set of three selected images, each containing the person of interest, and one of the images contains two people. In block  206 , the user provides a label via the labeler  104  that indicates the selected images contain the person of interest and the images and videos from the image collection are to be searched by the person finder  108  to identify those believed to contain the person of interest. In block  208 , the person identifier accesses the features and associated labels stored in the database  114  and determines a digital image collection subset  112  of images and videos believed to contain the person of interest. In block  210 , the digital image collection subset  112  is displayed on the display  332 . For example,  FIG. 5  shows images in the digital image collection subset  112 . The digital image collection subset contains labeled images  220 , images correctly believed to contain the person of interest  222 , and images incorrectly believed to contain the person of interest  224 . This is a consequence of the imperfect nature of current face detection and recognition technology. In block  212 , the user reviews the digital image collection subset  112  and can indicate the correctness of each image in the digital image collection subset  112 . This user indication of correctness is used to provide additional labels via the labeler  104  in block  214 . For example, the user indicates via the user interface that all of the images and videos correctly believes to contain the person of interest  222  of the digital image collection subset  112  do contain the person of interest. Each image and video of the digital image collection is then labeled with the name of the person of interest if it has been provided by the user. If the name of the person of interest has not been provided by the user, the name of the person of interest can be determined in some cases by the labeler  104 . The images and videos of the digital image collection subset  112  are examined for those having a label indicating the name of the person of interest and for which the person detector  110  determines contain only one person. Because the user has verified that the images and videos of the digital image collection subset  112  do contain the person of interest and the person detector  110  finds only a single person, the labeler  104  concludes that the name of the person in the associated label is the name of the person of interest. If the person detector  110  is an automatic error-prone algorithm, then the labeler  104  may need to implement a voting scheme if more than one image and videos have an associated label containing a person&#39;s name and the person detector  110  finds only one person, and the person&#39;s name in the associated label is not unanimous. For example, if there are 3 images among the digital image collection subset  112  that contain one detected person each by the person detector  110 , and each image has a label containing a person&#39;s name, and the names are: “Hannah”, “Hannah”, and “Holly”, then the voting scheme conducted by the labeled  104  determines that the person&#39;s name is “Hannah”. The labeler  104  then labels the images and videos of the digital image collection subset  112  with a label containing the name of the person of interest (e.g. “Hannah”). The user can review the name of the person of interest determined by the labeler  104  via the display. After the user indicates that the images and videos of the digital image collection subset  112  contain the person of interest, the message “Label as Hannah?” appears, and the user can confirm the determined name of the person of interest by pressing “yes”, or enter a different name for the person of interest by pressing “no”. If the labeler  104  cannot determine the name of the person of interest, then a currently unused identifier is assigned to the person of interest (e.g. “Person  12 ”), and the images and videos of the digital image collection subset  112  are labeled by the labeler  104  accordingly.  
      Alternatively, the labeler  104  can determine several candidate labels for the person of interest. The candidate labels can be displayed to the user in the form of a list. The list of candidate labels can be a list of labels that have been used in the past, or a list of the most likely labels for the current particular person of interest. The user can then select from the list the desired label for the person of interest.  
      Alternatively, if the labeler  104  cannot determine the name of the person of interest, the user can be asked to enter the name of the person of interest by displaying the message “Who is this?” on the display  332  and allowing the user to enter the name of the person of interest, which can then be used by the labeler  104  to label the images and videos of the digital image collection subset  112 .  
      The user can also indicate, via the user interface, those images of the images and videos of the digital image collection subset  112  do not contain the person of interest. The indicated images are then removed from the digital image collection subset  112 , and the remaining images can be labeled as previously described. The indicated images can be labeled to indicate that they do not contain the person of interest so that in future searches for that same person of interest, an image explicitly labeled as not containing the person of interest will not be shown to the user. For example,  FIG. 6  shows the digital image collection subset  112  after an image incorrectly believed to contain the person of interest is removed.  
       FIG. 7  is a flow diagram showing an alternative method for identifying images believed to contain a person of interest. In this embodiment, a user labels the people in one or more images or videos, initiates a search for a person of interest, and the system determines and displays images or videos from a subset of the digital image collection  102  believed to contain the person of interest. The displayed images can be reviewed by the user, and the user can indicate whether the displayed images do contain the person of interest. In addition, the user can verify or provide the name of the person of interest. Finally, based on the input from the user, the system can again determine a set of images believed to contain the person of interest.  
      In block  202 , images are displayed on the display  332 . In block  204 , the user selects images, where each image contains the person of interest. At least one of the selected images contains more than one person. In block  206 , the user provides labels via the labeler  104  to identify the people in the selected images. Preferably, the label does not indicate the location of persons within the image or video. Preferably, the label indicates the name of the person or people in the selected images or videos.  FIG. 8  shows two selected images and the associated labels  226  indicating the names of people in each of the two selected images. In block  207 , the user initiates a search for a person of interest. The person of interest is the name of a person that has been used as a label when labeling people in selected images. For example, the user initiates a search for images of “Jonah.” In block  208 , the person identifier accesses the features from the features extractor  106  and associated labels stored in the database  114  and determines the digital image collection subset  112  of images and videos believed to contain the person of interest. In block  210 , the digital image collection subset  112  is displayed on the display  332 .  FIG. 9  shows that the digital image collection subset  112  contains labeled images  220 , images correctly believed to contain the person of interest  222 , and images incorrectly believed to contain the person of interest  224 . This is a consequence of the imperfect nature of current face detection and recognition technology. In block  212 , the user reviews the digital image collection subset  112  and can indicate the correctness of each image in the digital image collection subset  112 . This user indication of correctness is used to provide additional labels via the labeler  104  in block  204 . For example, the user indicates via the user interface that all of the images and videos correctly believes to contain the person of interest  222  of the digital image collection subset  112  do contain the person of interest. The user can also indicate, via the user interface, those images of the images and videos of the digital image collection subset  112  do not contain the person of interest. The indicated images are then removed from the digital image collection subset  112 , and the remaining images can be labeled as previously described. Each image and video of the digital image collection subset  112  is then labeled with the name of the person of interest. The user can review the name of the person of interest determined by the labeler  104  via the display. After the user indicates that the images and videos of the digital image collection subset  112  contain the person of interest, the message “Label as Jonah?” appears, and the user can confirm the determined name of the person of interest by pressing “yes”, or enter a different name for the person of interest by pressing “no.”  FIG. 10  shows the digital image collection subset  112  after the user has removed images incorrectly believed to contain the person of interest, and an automatically generated label  228  used to label the images that have been reviewed by the user.  
      Note that the person of interest and images or videos can be selected by any user interface known in the art. For example, if the display  332  is a touch sensitive display, then the approximate location of the person of interest can be found by determining the location that the user touches the display  332 .  
       FIG. 11  describes the feature extractor  106  from  FIG. 2  in greater detail. The feature extractor  106  determines features related to people from images and videos in the digital image collection. These features are then user by the person finder  108  to find images or videos in the digital image collection believed to contain the person of interest. The feature extractor  106  determines two types of features related to people. The global feature detector  242  determines global features  246 . A global feature  246  is a feature that is independent of the identity or position of the individual in an image of video. For example, the identity of the photographer is a global feature because the photographer&#39;s identity is constant no matter how many people are in an image or video and is likewise independent of the position and identities of the people.  
      Additional global features  246  include:  
      Image/video file name.  
      Image/video capture time. Image capture time can be a precise minute in time, e.g. Mar. 27, 2004 at 10:17 AM. Or the image capture time can be less precise, e.g. 2004 or March 2004. The image capture time can be in the form of a probability distribution function e.g. Mar. 27, 2004 +/−2 days with 95% confidence. Often times the capture time is embedded in the file header of the digital image or video. For example, the EXIF image format (described at www.exif.org) allows the image or video capture device to store information associated with the image or video in the file header. The “Date\Time” entry is associated with the date and time the image was captured. In some cases, the digital image or video results from scanning film and the image capture time is determined by detection of the date printed into the image (as is often done at capture time) area, usually in the lower left comer of the image. The date a photograph is printed is often printed on the back of the print. Alternatively, some film systems contain a magnetic layer in the film for storing information such as the capture date.  
      Capture condition metadata (e.g. flash fire information, shutter speed, aperture, ISO, scene brightness, etc.) Geographic location. The location is preferably stored in units of latitude and longitude.  
      Scene environment information. Scene environment information is information derived from the pixel values of an image or video in regions not containing a person. For example, the mean value of the non-people regions in an image or video is an example of scene environment information. Another example of scene environment information is texture samples (e.g. a sampling of pixel values from a region of wallpaper in an image).  
      Geographic location and scene environment information are important clues to the identity of persons in the associated images. For example, a photographer&#39;s visit to grandmother&#39;s house could be the only location where grandmother is photographed. When two images are captured with similar geographic locations and environments, it is more likely that detected persons in the two images are the same as well.  
      Scene environment information can be used by the person detector  110  to register two images. This is useful when the people being photographed are mostly stationary, but the camera moves slightly between consecutive photographs. The scene environment information is used to register the two images, thereby aligning the positions of the people in the two frames. This alignment is used by the person finder  108  because when two persons have the same position in two images captured closely in time and registered, then the likelihood that the two people are the same individual is high.  
      The local feature detector  240  computes local features  244 . Local features are features directly relating to the appearance of a person in an image or video. Computation of these features for a person in an image or video requires knowledge of the position of the person. The local feature detector  240  is passed information related to the position of a person in an image of video from either the person detector  110 , or the database  114 , or both. The person detector  110  can be a manual operation where a user inputs the position of people in images and videos by outlining the people, indicating eye position, or the like. Preferable, the person detector  110  implements a face detection algorithm. Methods for detecting human faces are well known in the art of digital image processing. For example, a face detection method for finding human faces in images is described in the following article: Jones, M. J.; Viola, P., “Fast Multi-view Face Detection”,  IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2003.  
      An effective, person detector  110  is based on the image capture time associated with digital images and videos is described with regard to  FIG. 12A . The images and videos of the digital image collection  102  are analyzed by a face detector  270 , such as the aforementioned face detector by Jones and Viola. The face detector is tuned to provide detected people  274  while minimizing false detections. As a consequence, many people in images are not detected. This can be a consequence of, for example, having their back to the camera, or a hand over the face. The detected faces from the face detector  270  and the digital image collection  102  are passed to a capture time analyzer  272  to find images containing people that were missed by the face detector  270 . The capture time analyzer  272  operates on the idea that, when two images are captured very close in time, it is likely that if an individual appears in one image, then he or she also appears in the other image as well. In fact, this relationship can be determined with fairly good accuracy by analyzing large collections of images when the identities of persons in the images are known. For processing videos, face tracking technology is used to find the position of a person across frames of the video. One method of face tracking is video is described in U.S. Pat. No. 6,700,999, where motion analysis is used to track faces in video.  
       FIG. 12B  shows a plot of the relationship used by the capture time analyzer  272 . The plot shows the probability of a person appearing in a second image, given that the person appeared in a first image, as a function of the difference in image capture time between the images. As expected, when two images are captured in rapid succession, the likelihood that a person appears in one image and not the other is very low.  
      The capture time analyzer  272  examines images and videos in the digital image collection  110 . When a face is detected by the face detector  270  in a given image, then the probability that that same person appears in another image is calculated using the relationship shown in  FIG. 12B .  
      For example, assume that the face detector  270  detected two faces in one image, and a second image, captured only 1 second later, the face detector  270  found only one face. Assuming that the detected faces from the first image are true positives, the probability is quite high (0.99* 0.99) that the second image also contains two faces, but only one found by the face detector  270 . Then, the detected people  274  for the second image are the one face found by the face detector  270 , and second face with confidence 0.98. The position of the second face is not known, but can be estimated because, when the capture time difference is small, neither the camera nor the people being photographed tend to move quickly. Therefore, the position of the second face in the second image is estimated by the capture time analyzer  272 . For example, when an individual appears in two images, the relative face size (the ration of the size of the smaller face to the larger face) can be examined. When the capture times of two images containing the same person is small, the relative face size usually falls near 1, because the photographer, and the person being photographed and the camera settings are nearly constant. A lower limit of the relative face size is plotted as a function of difference in image capture times in  FIG. 12C . This scaling factor can be used in conjunction with the known face position of a face in a first image to estimate a region wherein the face appears in the second image.  
      Note that the method used by the capture time analyzer  272  can also be used to determine the likelihood that a person of interest in is a particular image or video by the person finder  108 .  
      Also, the database  114  stores information associated with labels from the labeler  104  of  FIG. 2 . When the label contains position information associated with the person, the local feature detector  240  can determine local features  244  associated with the person.  
      Once the position of a person is known, the local feature detector  240  can detect local features  244  associated with the person. Once a face position is known, the facial features (e.g. eyes, nose, mouth, etc.) can also be localized using well known methods such as described by Yuille et al. in, “Feature Extraction from Faces Using Deformable Templates,”  Int. Journal of Comp. Vis ., Vol. 8, Iss. 2, 1992, pp. 99-111. The authors describe a method of using energy minimization with template matching for locating the mouth, eye and iris/sclera boundary. Facial features can also be found using active appearance models as described by T. F. Cootes and C. J. Taylor “Constrained active appearance models”, 8 th International Conference on Computer Vision , volume 1, pages 748-754. IEEE Computer Society Press, July 2001. In the preferred embodiment, the method of locating facial feature points based on an active shape model of human faces described in “An automatic facial feature finding system for portrait images”, by Bolin and Chen in the Proceedings of IS&amp;T PICS conference, 2002 is used.  
      The local features  244  are quantitative descriptions of a person. Preferably, the person finder feature extractor  106  outputs one set of local features  244  and one set of global features  246  for each detected person. Preferably the local features  244  are based on the locations of  82  feature points associated with specific facial features, found using a method similar to the aforementioned active appearance model of Cootes et al. A visual representation of the local feature points for an image of a face is shown in  FIG. 12D  as an illustration. The local features can also be distances between specific feature points or angles formed by lines connecting sets of specific feature points, or coefficients of projecting the feature points onto principal components that describe the variability in facial appearance.  
      The features used are listed in Table 1 and their computations refer to the points on the face shown numbered in  FIG. 12D . Arc (Pn, Pm) is defined as  
         ∑     i   =   n       m   -   1       ⁢          Pn   -     P   ⁡     (     n   +   1     )                  
 
      where ∥Pn-Pm∥ refers to the Euclidean distance between feature points n and m. These arc-length features are divided by the inter-ocular distance to normalize across different face sizes. Point PC is the point located at the centroid of points  0  and  1  (i.e. the point exactly between the eyes). The facial measurements used here are derived from anthropometric measurements of human faces that have been shown to be relevant for judging gender, age, attractiveness and ethnicity (ref. “Anthropometry of the Head and Face” by Farkas (Ed.), 2 nd  edition, Raven Press, New York, 1994).  
               TABLE 1                          List of Ration Features                                 Name   Numerator   Denominator                       Eye-to-nose/Eye-to-mouth   PC-P2   PC-P32           Eye-to-mouth/Eye-to-chin   PC-P32   PC-P75           Head-to-chin/Eye-to-mouth   P62-P75   PC-P32           Head-to-eye/Eye-to-chin   P62-PC   PC-P75           Head-to-eye/Eye-to-mouth   P62-PC   PC-P32           Nose-to-chin/Eye-to-chin   P38-P75   PC-P75           Mouth-to-chin/Eye-to-chin   P35-P75   PC-P75           Head-to-nose/Nose-to-chin   P62-P2   P2-P75           Mouth-to-chin/Nose-to-chin   P35-P75   P2-P75           Jaw width/Face width   P78-P72   P56-P68           Eye-spacing/Nose width   P07-P13   P37-P39           Mouth-to-chin/Jaw width   P35-P75   P78-P72                      
 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                   
               
               
                 List of Arc Length Features 
               
            
           
           
               
               
               
            
               
                   
                 Name 
                 Computation 
               
               
                   
                   
               
               
                   
                 Mandibular arc 
                 Arc (P69, P81) 
               
               
                   
                 Supra-orbital arc 
                 (P56-P40) + Int (P40, P44) + (P44-P48) + 
               
               
                   
                   
                 Arc (P48, P52) + (P52-P68) 
               
               
                   
                 Upper-lip arc 
                 Arc (P23, P27) 
               
               
                   
                 Lower-lip arc 
                 Arc (P27, P30) + (P30-P23) 
               
               
                   
                   
               
            
           
         
       
     
      Color cues are easily extracted from the digital image or video once the person and facial features are located by the person finder  106 .  
      Alternatively, different local features can also be used. For example, an embodiment can be based upon the facial similarity metric described by M. Turk and A. Pentland. In “Eigenfaces for Recognition”.  Journal of Cognitive Neuroscience. Vol  3,  No.  1. 71-86, 1991. Facial descriptors are obtained by projecting the image of a face onto a set of principal component functions that describe the variability of facial appearance. The similarity between any two faces is measured by computing the Euclidean distance of the features obtained by projecting each face onto the same set of functions.  
      The local features  244  could include a combination of several disparate feature types such as Eigenfaces, facial measurements, color/texture information, wavelet features etc.  
      Alternatively, the local features  244  can additionally be represented with quantifiable descriptors such as eye color, skin color, face shape, presence of eyeglasses, description of clothing, description of hair, etc.  
      For example, Wiskott describes a method for detecting the presence of eyeglasses on a face in “Phantom Faces for Face Analysis”,  Pattern Recognition,  Vol. 30, No. 6, pp. 837-846, 1997. The local features contain information related to the presence and shape of glasses.  
       FIG. 12E  shows the areas in the image hypothesized to be the face region  282 , clothing region  284  and background region  286  based on the eye locations produced by the face detector. The sizes are measured in terms of the inter-ocular distance, or IOD (distance between the left and right eye location). The face covers an area of three times IOD by four times IOD as shown. The clothing area covers five times IOD and extends to the bottom of the image. The remaining area in the image is treated as the background. Note that some clothing area may be covered by other faces and clothing areas corresponding to those faces.  
      Images and videos in a digital image collection  102  are clustered into events and sub-events, according to U.S. Pat. No. 6,606,411 has consistent color distribution, and therefore, these pictures are likely to have been taken with the same backdrop. For each sub-event, a single color and texture representation is computed for all background areas taken together. The color and texture representations and similarity are derived from U.S. Pat. No. 6,480,840 by Zhu and Mehrotra. According to their method, color feature-based representation of an image is based on the assumption that significantly sized coherently colored regions of an image are perceptually significant. Therefore, colors of significantly sized coherently colored regions are considered to be perceptually significant colors. Therefore, for every input image, its coherent color histogram is first computed, where a coherent color histogram of an image is a function of the number of pixels of a particular color that belong to coherently colored regions. A pixel is considered to belong to a coherently colored region if its color is equal or similar to the colors of a pre-specified minimum number of neighboring pixels. Furthermore, texture feature-based representation of an image is based on the assumption that each perceptually significantly texture is composed of large numbers of repetitions of the same color transition(s). Therefore, by identifying the frequently occurring color transitions and analyzing their textural properties, perceptually significant textures can be extracted and represented.  
      The eye locations produced by the face detector are used to initialize the starting face position for facial feature finding.  FIG. 12F  shows the locations of the feature points on a face and the corresponding image patches where the named secondary features may be located.  
      Table 3 lists the bounding boxes for these image patches shown in  FIG. 12F , the hair region  502 , the bangs region  504 , the eyeglasses region  506 , the cheek region  508 , the long hair regions  510 , the beard region  512 , and the mustache region  514 , where Pn refers to facial point number n from  FIG. 12F  or  FIG. 12D  and [x] and [y] refer to the x and y-coordinate of the point. (Pn-Pm) is the Euclidean distance between points n and m. The “cheek” and “hair” patches are treated as reference patches (denoted by [R] in the table) depicting a feature-less region of the face and the person&#39;s hair respectively. Secondary features are computed as gray-scale histogram difference between the potential patch containing the secondary feature and the appropriate reference patch. Left and right patches are combined to generate the histograms for each secondary feature. The histograms are normalized by the number of pixels so that the relative sizes of the patches being compared are not a factor in the difference computed. Secondary features are treated as binary features—they are either present or absent. A threshold is used to ascertain whether the secondary feature is present. Table 4 gives a table showing the histogram differences used for each of the secondary features to be detected.  
               TABLE 3                          Bounding boxes of facial feature regions.                         Bounding box                                     x-start   y-start   width   height                                             Cheek[R]   P80[x] + ⅓   Mean (P80[y], P81[y])   ⅔ (P37-P80)   P79-P80       (right)   (P37-P80)       Cheek[R]   P39[x]   Mean (P69[y], P70[y])   ⅔ (P39-P70)   P70-P69       (left)       Hair[R]   P61[x]   P62[y] − height   P63-P61   P68-P17       Long hair   P56[x] − 2*width   P56[y]   P56-P3   P56-P79       (left)       Long hair   P68[x] + width   P68[y]   P68-P17   P71-P68       (right)       Eyeglass   P56[x] + ⅓   Mean (P56[y], P81[y])   ⅔ (P7-P56)   ½ (P56-P81)       (left)   (P7-P56)       Eyeglass   P13[x]   Mean (P68[y], P69[y])   ⅔ (P13-P68)   ½ (P69-P68)       (right)       Bangs   P60[x]   Mean (P60[y], P64[y])   P64-P60   ⅔ (P42-P60)       Mustache   P23[x]   P38[y]   P27-P23   P38-P25       Beard   Mean (P30[x],   Mean (P75[y], P35[y])   Mean (P28-P30,   ½ (P75-P35)           P76[x]       P74-P76)                  
 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                   
               
               
                 histogram differences for secondary features. 
               
            
           
           
               
               
               
            
               
                   
                 Feature 
                 Histogram difference test 
               
               
                   
                   
               
               
                   
                 Long hair 
                 Long hair − Hair &lt; threshold 
               
               
                   
                 Eyeglass 
                 Eyeglass − Cheek &gt; threshold 
               
               
                   
                 Bangs 
                 Bangs − Cheek &gt; threshold 
               
               
                   
                 Mustache 
                 Mustache − Cheek &gt; threshold 
               
               
                   
                 Beard 
                 Beard − Cheek &gt; threshold 
               
               
                   
                   
               
            
           
         
       
     
      Again referring to  FIG. 11 , the global features  246  and local features  244  are stored in the database  114 . Global features associated with all people in an image are represented by F G . The N sets of local features associated with the N people in an image are represented as F L0 , F L1 , . . . , F LN−1 . The complete set of features for a person n in the image is represented as F n , and includes the global features F G  and the local features F Ln . The M labels associated with the image are represented as L 0 , L 1 , . . . , L M−1 . When the label does not include the position of the person, there is ambiguity in knowing which label is associated with which set of features representing persons in the image or video. For example, when there are two sets of features describing two people in an image and two labels, it is not obvious which features belongs with which label. The person finder  108  solves this constrained classification problem of matching labels with sets of local features, where the labels and the local features are associated with a single image. There can be any number of labels and local features, and even a different number of each.  
      Here is an example entry of labels and features associated with an image in the database  114 : 
      Image  101 _ 346 .JPG     Label L 0 : Hannah     Label L 1 : Jonah     Features F 0 : 
        Global Features F G : 
            Capture Time: Aug. 7, 2005, 6:41 PM EST.     Flash Fire: No     Shutter Speed: 1/724 sec.     Camera Model: Kodak C360 Zoom Digital Camera     Aperture: F/2.7     Environment:    
            Local Features F LO : 
            Position: Left Eye: [1400 198] Right Eye: [1548 202 ]    C 0 =[−0.8, −0.01]′;     Glasses: none    
            Associated Label: Unknown    
        Features F 1 : 
        Global Features F G : 
            Capture Time: Aug. 7, 2005, 6:41 PM EST.     Flash Fire: No     Shutter Speed: 1/724 sec.     Camera Model: Kodak C360 Zoom Digital Camera     Aperture: F/2.7     Environment:    
            Local Features: F L1 : 
            Position: Left Eye: [810 192] Right Eye: [956 190]    C 1 =[0.06, 0.26]′;     Glasses: none    
            Associated Label: Unknown    
       

       FIG. 13  describes the person finder  108  of  FIG. 2  in greater detail. A person identifier  250  considers the features and labels in the database  114  and determines the identity (i.e. determines a set of related features) of people in images that were labeled with labels not containing the position of the person. The person identifier  250  associates features from the feature extractor  106  with labels from the labeler  104 , thereby identifying person in an image or video. The person identifier  250  updates the features from the database and produces modified features  254  that are stored in the database  114 . As an example, consider the images shown in  FIG. 8 . The first image  260  contains 2 people, who according to the labels  226  are Hannah and Jonah. However, it is not known which person is Hannah and which is Jonah because the labels do not contain position. The second image  262  is labeled Hannah. Because there is only one person, that person can be identified with high confidence as Hannah. The person identifier  250  can determine the identities of the people in the first image  260  by using features related to Hannah from the second image  262  and comparing the features of the people in the first image  260 . A person  266  has features similar to the features to a person  264  identified as Hannah in the second image  262 . The person identifier  250  can conclude, with high confidence, that person  266  in the first image  260  is Hannah, and by elimination person  268  is Jonah. The label  226  Hannah for the first image  260  is associated with the global features F G  for the image and the local features associated with the person  266 . The label  226  Jonah for the first image  260  is associated with the global features for the image and the local features associated with the person  268 . Since the identities of the people are determined, the user can initiate a search for either Hannah or Jonah using the appropriate features.  
      Generally speaking, the person identifier  250  solves a classification problem. The problem is to associate labels not having position information with local features, where the labels and the local features are both associated with the same image. An algorithm to solve this problem is implemented by the person identifier  250 .  FIG. 14  shows a representation of actual local features computed from a digital image collection. The positions of 15 sets of local features are marked on the plot. The symbol used to represent the mark indicates the true identity of a person associated with the local features “x” for Hannah, “+” for Jonah, “*” for Holly, and “□” (a box) for Andy. Each set of local features could be associated with any of the labels assigned to the image. Near each set of local features marked on the plot are the possible labels that could be associated with the local features “A” for Andy, “H” for Hannah, “J” for Jonah, and “O” for Holly. The table below shows the data. Links between marks on the plot indicate that the sets of local features are from the same image. The algorithm used to assign local features to labels works by finding an assignment of local features to labels that minimizes the collective variance (i.e. the sum of the spread of the data points assigned to each person) of the data points. The assignments of local features to labels are subject to the constraint that a label can only be used once for each image (i.e. once for each set of data points connected by links). Preferably, the collective variance is computed as the sum over each data point of the squared distance from the data point to the centroid of all data points assigned to that same individual.  
      The algorithm for classifying the local features can be summarized by the equation:  
         min     d   j       ⁢       ∑   j     ⁢         (       c     d   j       -     f   j       )     T     ⁢     (       c     d   j       -     f   j       )             
          Where:     f j  represents the j th  set of local features     d j  represents the class (i.e. the identity of the individual) that the j th  set of local features is assigned to     C d     j    represents the centroid of the class that the j th  set of local features is assigned to        

      The expression is minimized by choosing the assignments of the class for each of the j th  set of local features.  
      In this equation, a Euclidean distance measure is used. Those skilled in the art will recognize that many different distance measures, such as Mahalanobis distance, or the minimum distance between the current data point and another data point assigned to the same class, can be used as well.  
      This algorithm correctly associates all 15 local features in the example with the correct label. Although in this example the number of labels and the number of sets of local features in each image was the same in the case of each image, which is not necessary for the algorithm used by the person identifier  250  to be useful. For example, a user can provide only two labels for an image containing three people and from which three sets of local features are derived.  
      In some cases, the modified features  254  form the person identifier  250  are straightforward to generate from the database  114 . For example, when the database contains only global features and no local features, then the features associated with each label (whether or not the label contains position information) will be identical. For example, if the only feature is image capture time, then each label associated with the image is associated with the image capture time. Also, if the labels contain position information, then associating features with the labels is easy because either the features do not include local features and therefore the same features are associated with each label, or the features contain local features and the position of the image region over which the local features are computed is used to associate the features with the labels (based on proximity).  
      A person classifier  256  uses the modified features  254  and a identity of the person of interest  252  to determine a digital image collection subset  112  of images and videos believed to contain the person of interest. The modified features  254  includes some features having associated labels (known as labeled features). Other features (known as unlabeled features) do not have associated labels (e.g. all of the image and videos in the digital image collection  102  that were not labeled by the labeler  104 ). The person classifier  256  uses labeled features to classify the unlabeled features. This problem, although in practice quite difficult, is studied in the field of pattern recognition. Any classifier may by used to classify the unlabeled features. Preferably, the person classifier determines a proposed label for each of the unlabeled features and a confidence, belief, or probability associated with the proposed label. In general, classifiers assign labels to unlabeled featured by considering the similarity between a particular set of unlabeled features and labeled sets of features. With some classifiers (e.g. Gaussian Maximum Likelihood), labeled sets of features associated with a single individual person are aggregated to form a model of appearance for the individual. The digital image collection subset  112  is the collection of images and videos having an associated proposed label with a probability that exceeds a threshold T 0 , where T 0  ranges from 0&lt;=T 0 &lt;=1.0. Preferably, the digital image collection subset  112  also contains the image and videos associated with features having labels matching the identity of the person of interest  252 . The images and videos of the digital image collection subset are sorted so that images and videos determined to have the highest belief of containing the person of interest appear at the top of the subset, following only the images and videos with features having labels matching the identity of the person of interest  252 .  
      The person classifier  256  can measure the similarity between sets of features associated with two or more persons to determine the similarity of the persons, and thereby the likelihood that the persons are the same. Measuring the similarity of sets of features is accomplished by measuring the similarity of subsets of the features. For example, when the local features describe clothing, the following method is used to compare two sets of features. If the difference in image capture time is small (i.e. less than a few hours) and if the quantitative description of the clothing is similar in each of the two sets of features is similar, then the likelihood of the two sets of local features belonging to the same person is increased. If, additionally, the clothes have a very unique or distinctive pattern (e.g. a shirt of large green, red, and blue patches) for both sets of local features, then the likelihood is even greater that the associated people are the same individual.  
      Clothing can be represented in different ways. The color and texture representations and similarity described in U.S. Pat. No. 6,480,840 by Zhu and Mehrotra is one possible way. In another possible representation, Zhu and Mehrotra describe a method specifically intended for representing and matching patterns such as those found in textiles in U.S. Pat. No. 6,584,465. This method is color invariant and uses histograms of edge directions as features. Alternatively, features derived from the edge maps or Fourier transform coefficients of the clothing patch images can be used as features for matching. Before computing edge-based or Fourier-based features, the patches are normalized to the same size to make the frequency of edges invariant to distance of the subject from the camera/zoom. A multiplicative factor is computed which transforms the inter-ocular distance of a detected face to a standard inter-ocular distance. Since the patch size is computed from the inter-ocular distance, the clothing patch is then sub-sampled or expanded by this factor to correspond to the standard-sized face.  
      A uniqueness measure is computed for each clothing pattern that determines the contribution of a match or mismatch to the overall match score for persons, as shown in Table 5, where + indicates a positive contribution and − indicates a negative contribution, with the number of + or − used to indicate the strength of the contribution. The uniqueness score is computed as the sum of uniqueness of the pattern and the uniqueness of the color. The uniqueness of the pattern is proportional to the number of Fourier coefficients above a threshold in the Fourier transform of the patch. For example, a plain patch and a patch with single equally spaced stripes have 1 (dc only) and 2 coefficients respectively, and thus have low uniqueness score. The more complex the pattern, the higher the number of coefficients that will be needed to describe it, and the higher its uniqueness score. The uniqueness of color is measured by learning, from a large database of images of people, the likelihood that a particular color occurs in clothing. For example, the likelihood of a person wearing a white shirt is much greater than the likelihood of a person wearing an orange and green shirt. Alternatively, in the absence of reliable likelihood statistics, the color uniqueness is based on its saturation, since saturated colors are both rarer and also can be matched with less ambiguity. In this manner, clothing similarity or dissimilarity, as well as the uniqueness of the clothing, taken with the capture time of the images are important features for the person classifier  256  to recognize a person of interest.  
      Clothing uniqueness is measured by learning, from a large database of images of people, the likelihood that particular clothing appears. For example, the likelihood of a person wearing a white shirt is much greater than the likelihood of a person wearing an orange and green plaid shirt. In this manner, clothing similarity or dissimilarity, as well as the uniqueness of the clothing, taken with the capture time of the images are important features for the person classifier  256  to recognize a person of interest.  
               TABLE 5                          The effect of clothing on likelihood of       two people being the same individual                                 Time   Clothing Uniqueness                                     Interval   common   rare                                                 Same event   Match   ++   +++               Not Match   −−−   −−−           Different   Match   +   +++           Event   Not Match   No effect   No effect                      
 
      Table 5 shows the how the likelihood of two people is affected by using a description of clothing. When the two people are from images or videos from the same event, then the likelihood of the people being the same individual decreases (- - -) a large amount when the clothing does not match. The “same event” means that the images have only a small difference between image capture time (i.e. less than a few hours), or that they have been classified as belonging to the same event either by a user or by an algorithm such as described in U.S. Pat. No. 6,606,411. Briefly summarized, a collection of images are classified into one or more events determining one or more largest time differences of the collection of images based on time and/or date clustering of the images and separating the plurality of images into the events based on having one or more boundaries between events which one or more boundaries correspond to the one or more largest time differences.  
      When the clothing of two people matches and the images are from the same event, then the likelihood that the two people are the same individual depends on the uniqueness of the clothing. The more unique the clothing that matches between the two people, the greater the likelihood that the two people are the same individual.  
      When the two people are from images belonging to different events, a mismatch between the clothing has no effect on the likelihood that the people are the same individuals (as it is likely that people change clothing).  
      Preferably, the user can adjust the value of T 0  through the user interface. As the value increases, the digital image collection subset  112  contains fewer images or videos, but the likelihood that the images and videos in the digital image collection subset  112  actually do contain the person of interest increases. In this manner, the user can determine the number and accuracy of the search results.  
      The invention can be generalized beyond recognizing people, to a general object recognition method as shown in  FIG. 15 , which is similar to  FIG. 2 . A digital image collection  102  containing objects is searched for an object of interest by a person finder  408 . The digital image collection subset  112  is displayed on the display  332  for review by the human user.  
      The search for an object of interest is initiated by a user as follows: Images or videos of the digital image collection  102  are displayed on the display  332  and viewed by the user. The user establishes one or more labels for one or more of the images with a labeler  104 . A feature extractor  106  extracts features from the digital image collection in association with the label(s) from the labeler  104 . The features are stored in association with labels in a database  114 . An object detector  410  can optionally be used to assist in the labeling and feature extraction. When the digital image collection subset  112  is displayed on the display  332 , the user can review the results and further label the displayed images.  
      A label from the labeler  104  indicates that a particular image or video contains a person of interest and includes at least one of the following:  
      (1) the name of an object of interest in an image or video.  
      (2) an identifier associated with the person of interest such as a text string or identifier such as “Object A” or “Object B”.  
      (3) the location of the object of interest within the image or video. Preferably, the location of the object of interest is specified by coordinates of a box that surrounds the object of interest. The user can indicate the location of the object of interest by using a mouse to click on the positions of the eyes for example. When an object detector  410  detects an object, the position of the object can be highlighted to the user by, for example, circling the object on the display  332 . Then the user can provide the name or identifier for the highlighted object, thereby associating the position of the object with the user provided label.  
      (4) an indication to search for images or videos from the image collection believed to contain the object of interest.  
      (5) the name or identifier of an object of interest who is not in the image. For example, the object of interest can be a person, face, car, vehicle, or animal.  
      Those skilled in the art will recognize that many variations may be made to the description of the present invention without significantly deviating from the scope of the present invention.  
     Parts List  
     
         
           10  image capture  
           25  background areas taken together  
           40  general control computer  
           102  digital image collection  
           104  labeler  
           106  feature extractor  
           108  person finder  
           110  person detector  
           112  digital image collection subset  
           114  database  
           202  block  
           204  block  
           206  block  
           207  block  
           208  block  
           210  block  
           212  block  
           214  block  
           220  labeled image  
           222  image correctly believed to contain the person of interest  
           224  image incorrectly believed to contain the person of interest  
           226  label  
           228  generated label  
           240  local feature detector  
           242  global feature detector  
           244  focal features  
           246  global features  
           250  person identifier  
           252  identity of person of interest  
           254  modifies features 
 
 List Cont&#39;d 
 
           256  person classifier  
           260  first image  
           262  second image  
           264  person  
           266  person  
           268  person  
           270  face detector  
           272  capture time analyzer  
           274  detected people  
           282  face region  
           284  clothing region  
           286  background region  
           310  digital camera phone  
           303  flash  
           305  lens  
           311  CMOS image sensor  
           312  timing generator  
           314  image sensor array  
           316  A/D converter circuit  
           318  DRAM buffer memory  
           320  digital processor  
           322  RAM memory  
           324  real-time clock  
           325  location determiner  
           328  firmware memory  
           330  image/data memory  
           332  color display  
           334  user controls 
 
 List Cont&#39;d 
 
           340  audio codec  
           342  microphone  
           344  speaker  
           350  wireless modem  
           352  RF channel  
           358  phone network  
           362  dock interface  
           364  dock/charger  
           370  Internet  
           372  service provider  
           408  object finder  
           410  object detector  
           502  hair region  
           504  bang region  
           506  eyeglasses region  
           508  cheek region  
           510  long hair region  
           512  beard region  
           514  mustache region