Patent Publication Number: US-6671391-B1

Title: Pose-adaptive face detection system and process

Description:
BACKGROUND 
     1. Technical Field 
     This invention is directed toward a face detection system and process for detecting the presence of faces of people depicted in an input image, and more particularly to such a face detection system and process that also identifies the face pose of each detected face. 
     2. Background Art 
     Face detection systems essentially operate by scanning an image for regions having attributes which would indicate that a region contains a person&#39;s face. To date, current systems are very limited in that detection is only possible in regions associated with a frontal view of a person&#39;s face. In addition, current detection systems have difficulty in detecting face regions in images having different lighting conditions or faces at different scales than the system was initially designed to handle. 
     The problem of detecting the faces of people depicted in an image from the appearance of their face has been studied for many years. Face recognition systems and processes essentially operate by comparing some type of training images depicting people&#39;s faces (or representations thereof) to an image or representation of a person&#39;s face extracted from an input image. In the past, most of these systems required that both the original training images and the input image region be essentially frontal views of the person. This is limiting in that to obtain the input images containing a frontal view of the face of the person being identified, that person had to either be purposefully positioned in front of a camera, or a frontal view had to be found and extracted from a non-staged input image (assuming such a frontal view exists in the image). 
     More recently there have been attempts to build a face detection and recognition system that works with faces rotated out of plane. For example, one approach for recognizing faces under varying poses is the Active Appearance Model proposed by Cootes et al. [1], which deforms a generic 3-D face model to fit the input image and uses control parameters as a feature fed to a classifier. Another approach is based on transforming an input image into stored prototypical faces and then using direct template matching to recognize the person whose face is depicted in the input image. This method is explored in the papers by Beymer [2], Poggio [3] and Vetter [4]. 
     It is noted that in the preceding paragraphs, as well as in the remainder of this specification, the description refers to various individual publications identified by a numeric designator contained within a pair of brackets. For example, such a reference may be identified by reciting, “reference [1]” or simply “[1]”. A listing of the publications corresponding to each designator can be found at the end of the Detailed Description section. 
     SUMMARY 
     The present invention is directed toward a face detection system and process that overcomes the aforementioned limitations in prior face detection and recognition systems by making it possible to detect a person&#39;s face in input images containing either frontal or non-frontal views of the person&#39;s face, regardless of the scale or illumination conditions associated with the face. Thus, a non-staged image, such as a frame from a video camera monitoring a scene, can be searched to detect a region depicting the face of a person, without regard to whether the person is directly facing at the camera. Essentially, as long as the person&#39;s face is visible in the image being searched, the present face detection system can be used to detect the location of the face in the image. To date there have not been any face detection systems that could detect a person&#39;s face in non-frontal views. In addition, the present invention can be used to not only detect a person&#39;s face, but also provide pose information. This pose information can be quite useful. For example, knowing which way a person is facing can be useful in user interface and interactive applications where a system would respond differently depending on where a person is looking. Having pose information can also be useful in making more accurate 3D reconstructions from images of the scene. For instance, knowing that a person is facing another person can indicate the first person is talking to the second person. This is useful in such applications as virtual meeting reconstructions. 
     Because the present face detection system and associated process can be used to detect both frontal and non-frontal views of a person&#39;s face, it is termed a pose-adaptive face detection system. For convenience in describing the system and process, the term “pose” will refer to the particular pitch, roll and yaw angles that describe the position of a person&#39;s head (where the 0 degree pitch, roll and yaw position corresponds to a person facing the camera with their face centered about the camera&#39;s optical axis). 
     The pose-adaptive face detection system and process must first be trained before it can detect face regions in an input image. This training phase generally involves first capturing images of the faces of a plurality of people. As will be explained later, the captured face images will be used to train a series of Support Vector Machines (SVMs). Each SVM will be dedicated to a particular face pose, or more precisely a pose range. Accordingly, the captured face images should depict people having a variety of face poses. Only those face images depicting a person with a face pose that falls within the particular pose range of a SVM will be used to train that SVM. It is noted that the more diverse the training face images are, the more accurate the detecting capability of the SVM will become. Thus, it is preferred that the face images depict people which are not generally too similar in appearance. The training images can be captured in a variety of ways. One preferred method would involve positioning a subject in front of a video camera and capturing images (i.e., video frames) as the subject moves his or her head in a prescribed manner. 
     The captured face images are preprocessed to prepare them for input into the appropriate SVM. In general, this will involve normalizing, cropping, categorizing and finally abstracting the face images. Normalizing the training images preferably entails normalizing the scale of the images by resizing the images. It is noted that this action could be skipped if the images are captured at the desired scale thus eliminating the need for resizing. The desired scale for the face images is approximately the size of the smallest face region expected to be found in the input images that are to be searched. In a tested embodiment of the present invention, an image size of about 20 by 20 pixels was used with success. The image could additionally be normalized in regards to the eye locations within the image. In other words, each image would be adjusted so that the eye locations fell within a prescribed area. These normalization actions are performed so that each of the training images generally match as to orientation and size. The images are also preferably cropped to eliminate unneeded portions which could contribute to noise in the upcoming abstraction process. It is noted that the training images could be cropped first and then normalized, if desired. It is also noted that a histogram equalization, or similar procedure, could be employed to reduce the effects of illumination differences in the images that could introduce noise into the detecting process. 
     The next action in the training image preprocessing procedure involves categorizing the normalized and cropped images according to their pose. One preferred way of accomplishing this action is to group the images into a set of prescribed pose ranges. It is noted that the persons in the training images could be depicted with any combination of pitch, roll and yaw angles, as long as at least a portion of their face is visible. In such a case, the normalized and cropped images would be categorized into pose ranges defined by all three directional angles. The size of these pose ranges will depend on the application and the accuracy desired, but can be readily determined and optimized via conventional means. 
     The abstracting procedure is essentially a method of representing the images in a simpler form to reduce the processing load associated with the SVM&#39;s detection operation. While many abstraction processes might be employed for this purpose (e.g., histograming, Hausdorff distance, geometric hashing, active blobs, eigenface representations, and others), the preferred method entails the use of wavelet transforms, and particularly the use of three types of non-standard Haar wavelet transforms to represent each normalized, cropped, and categorized training face image, in a manner similar to that discussed in Oren [5]. Oren [5] discusses an image representation which captures the relationship between average intensities of neighboring image regions through the use of a family of basis functions, specifically Haar wavelets, which encode such relationships along different orientations. To this end, three types of 2-dimensional Haar wavelets are employed. These types include basis functions which capture change in intensity along the horizontal direction, the vertical direction and the diagonals (or corners). This Haar wavelet transform process is repeated to produce wavelet coefficients at two different scales, e.g., at 4×4 pixels and 2×2 pixels. 
     The result of the wavelet transform process is a series of coefficients. For each face pose range, a particular sub-set of these coefficients are selected to form a set of so-called feature vectors, although the same number of coefficients is used to make up each feature vector. It is noted that a different combination of coefficients may be needed to make up a feature vector associated with each pose range group. Thus, each training image is actually represented by a unique set of the computed feature vectors—one for each of the SVMs, tailored for each face pose range. Furthermore, all these feature vectors will be used to train the ensemble neural network. 
     This tailoring process begins by calculating the mean coefficients of all the training images. To this end, all the training images depicting a person exhibiting a face pose within a particular pose range are selected. The mean of all the horizontal wavelet coefficients associated with a particular pixel location of the selected training images that were computed under the first scale (e.g., 4×4), and the mean of all the horizontal wavelet coefficients associated with the pixel location that were computed under the other scale (e.g., 2×2), are calculated. The normalizing and cropping steps described previously will create training images that have the same size, and so the same number of corresponding pixel locations. This process is then repeated for each pixel location of the images. The process of computing the means of the wavelet coefficients associated with both scales for each pixel location is then repeated for the vertical wavelet coefficients and the diagonal wavelet coefficients. Thus, once all the coefficient means have been computed, there will be two average horizontal coefficients (i.e., one for each scale), as well as two average vertical and two average diagonal coefficients, associated with each pixel location of the training images. Those mean coefficients that have values outside a prescribed coefficient range are then identified. The pixel location and pedigree (i.e., direction and scale) of the identified mean coefficients are designated as the coefficients that will be used to form a feature vector for the particular pose range associated with the selected training images. The foregoing selection process is then repeated for the training images depicting faces exhibiting each of the remaining pose ranges. In this way a specific group of wavelet coefficients, as would be derived from any image undergoing the previously-described abstraction process, are identified for inclusion in a feature vector representing each of the pose ranges. 
     The prepared face image representations are used to train a 2-stage classifier which includes a bank of SVMs as an initial pre-classifier layer, and a neural network forming a subsequent decision classifier layer. As indicated previously, the bank of SVMs is composed of a plurality of SVMs each of which is trained to detect faces exhibiting a particular range of poses. To this end, the output of each SVM would indicate whether an input image region is a face having a pose within the SVM&#39;s range, or it is not. While this output could be binary (i.e., yes or no), it is preferred that a real-value output be produced. Essentially, the real-value output of each SVM pre-classifier is indicative of the distance of an input feature vector associated an input image region from a decision hyperplane defined by the face images used to train the SVM. In other words, the output indicates how closely an input feature vector fits into the face class represented by the SVM. Ideally, each SVM would be configured such that an image region that is not a face exhibiting a pose range associated with the SVM, or not depicting a face at all, is indicated by a negative output value. If this were the case, the face detection and pose information could be derived directly from the SVM outputs. However, this ideal is hard to achieve, especially for input regions depicting a face just outside the pose range of an SVM. In reality, such image regions may produce relatively low, but positive output values. Thus, a definitive indication of the pose range could not be made easily. This is where the second stage neural network comes into play. The single neural network forming the second stage of the face detection system architecture acts as a “fusing” neural network that combines or fuses the outputs from each of the first stage SVMs. Whereas one SVM alone cannot provide a definitive indication of the face pose range of an input face region, collectively, they can when all their outputs are considered via the fusing inherent in a neural network. 
     As indicated previously, the system must be trained before detecting faces and identifying face poses in an input image can be attempted. The SVMs are trained individually first, and then the neural network is trained using the outputs of the SVMs. Each SVM is trained by sequentially inputting the feature vectors derived from the training images associated with the same pose range category—i.e., the category to which the SVM is to dedicated. As usual the corresponding elements of each feature vector are input into the same input nodes of the SVM. Interspersed with these face image feature vectors are so-called negative examples that the SVM is instructed are not face images. A negative example is a feature vector created in the same way as the face image feature vectors, except that the image used is not of a face exhibiting a pose within the range being associated with the SVM. Initially, the images associated with the negative example vectors preferably depict “natural” scenes not containing faces. However, a problem can arise in that there is no typical example of the negative class. In other words, the number of different scenes not depicting a face are nearly infinite. To overcome this problem, a “bootstrapping” technique is employed. First, it must be noted that the aforementioned training image feature vectors and negative example vectors are input into the SVM repeatedly until the output of the SVM stabilizes (i.e., does not vary outside a prescribed threshold between training iterations for each corresponding inputted vector). Bootstrapping comes into play by introducing face images that have poses that fall outside the designated range of the SVM being trained once the outputs have stabilized. The feature vectors produced from these images are fed into the SVM without any indication that they are negative examples. Whenever one of these negative examples derived from an “out-of-range” face image results in an output that indicates the face image is within the pose range associated with the SVM (i.e., a false alarm), the SVM is instructed that the input is a negative example. Such bootstrapping results in a more accurate SVM. The foregoing training procedure is repeated for each of the SVMs. 
     Once all the SVMs have been trained, the neural network is brought on-line and the set of feature vectors associated with each respective training image is, in turn, simultaneously input into the appropriate SVM. This is repeated until the outputs of the neural network stabilize. The sequence in which the feature vectors sets are input can be any desired. However, it is believed that inputting the vector sets in random pose range order will cause the neural network to stabilize more quickly. Finally, at least one face image feature vector set representing each pose range is, in turn, simultaneously input into the appropriate SVM, and the active output of the neural network is assigned as corresponding to a face being detected in an input image region and having the pose associated with the training image used to create the feature vector causing the output. The remaining neural network outputs (which will number at least one) are assigned as corresponding to a face not being detected in an input image region. 
     The system is now ready to accept prepared input image regions, and to indicate if the region depicts a face, as well as indicating the pose range exhibited by the face. To this end, the input image being searched is divided into regions. For example, a moving window approach can be taken where a window of a prescribed size is moved across the image, and at prescribed intervals, all the pixel within the window become the next image region to be tested for a face. However, it is not known what size a face depicted in an input image may be, and so the size of the window must be considered. One way of ensuring that a face of any practical size depicted in an input image is captured in the window is to adopt an image pyramid approach. In this approach the window size is selected so as to be the smallest practical. In other words, the window size is chosen to be the size of the smallest detectable face in an input image. This window size should also match the size chosen for the training face images used to train the system. For a tested embodiment of the present face detection system and process, a window size of 20 by 20 pixels was chosen. Of course, many or all of the faces depicted in an input image will likely be larger than the aforementioned window size. Thus, the window would only cover a portion of the bigger faces and detection would be unlikely. This is solved by not only searching the original input image with the search window (in order to find the “smallest” faces), but by also searching a series of reduce scaled versions of the original input image. For example, the original image can be reduced in scale in a stepwise fashion all the way down to the size of the search window itself, if desired. After each reduction in scale, the resulting image would be searched with the search window. In this way, larger faces in the original image would be made smaller and will eventually reach a size that fits into the search window. 
     Each input image region extracted in the foregoing manner from any scale version of the input image is abstracted in a way similar to the training images to produce a set of feature vectors, which are, in turn, simultaneously input into the appropriate SVMs. For each feature vector set input the system, an output is produced from the neural network having one active node. The active node will indicate first whether the region under consideration depicts a face, and secondly, if a face is present, into what pose range the pose of the face falls. 
     In an alternate embodiment of the foregoing search procedure, instead of using the input image (or scaled down versions thereof) directly and then abstracting each extracted region, the entire input image (or scaled down versions thereof) could be abstracted first and then each extracted region could be feed directly into the SVMs. 
    
    
     DESCRIPTION OF THE DRAWINGS 
     The specific features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where: 
     FIG. 1 is a diagram depicting a general purpose computing device constituting an exemplary system for implementing the present invention. 
     FIG. 2 is a diagram diagramming an overall face detection process for detecting a face depicted in an input image and the face pose of each detected person. 
     FIG. 3 is a flow diagram of a process for accomplishing the preprocessing module of the overall process of FIG.  2 . 
     FIG. 4A is a flow diagram of a process for accomplishing, in part, the abstraction procedure of FIG.  3 . 
     FIG. 4B is a flow diagram depicting the continuation of the abstraction procedure shown in FIG.  4 A. 
     FIG. 5 is a block diagram of a SVM/neural network architecture that could be employed to accomplish the overall process or FIG.  2 . 
     FIG. 6A is a flow diagram of a process for the SVM and neural network training modules of the overall process of FIG.  2 . 
     FIG. 6B is a flow diagram depicting the continuation of the process shown in FIG.  6 A. 
     FIG. 7 is a flow diagram of a process for accomplishing the program modules of the overall process of FIG. 2 for detecting face images and identifying associated face poses by inputting an image into a SVM/neural network ensemble. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     In the following description of the preferred embodiments of the present invention, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. It is understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention. 
     FIG.  1  and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. Although not required, the invention will be described in the general context of computer-executable instructions, such as program modules, being executed by a personal computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     With reference to FIG. 1, an exemplary system for implementing the invention includes a general purpose computing device in the form of a conventional personal computer  20 , including a processing unit  21 , a system memory  22 , and a system bus  23  that couples various system components including the system memory to the processing unit  21 . The system bus  23  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory includes read only memory (ROM)  24  and random access memory (RAM)  25 . A basic input/output system  26  (BIOS), containing the basic routine that helps to transfer information between elements within the personal computer  20 , such as during start-up, is stored in ROM  24 . The personal computer  20  further includes a hard disk drive  27  for reading from and writing to a hard disk, not shown, a magnetic disk drive  28  for reading from or writing to a removable magnetic disk  29 , and an optical disk drive  30  for reading from or writing to a removable optical disk  31  such as a CD ROM or other optical media. The hard disk drive  27 , magnetic disk drive  28 , and optical disk drive  30  are connected to the system bus  23  by a hard disk drive interface  32 , a magnetic disk drive interface  33 , and an optical drive interface  34 , respectively. The drives and their associated computer-readable media provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for the personal computer  20 . Although the exemplary environment described herein employs a hard disk, a removable magnetic disk  29  and a removable optical disk  31 , it should be appreciated by those skilled in the art that other types of computer readable media which can store data that is accessible by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memories (RAMs), read only memories (ROMs), and the like, may also be used in the exemplary operating environment. 
     A number of program modules may be stored on the hard disk, magnetic disk  29 , optical disk  31 , ROM  24  or RAM  25 , including an operating system  35 , one or more application programs  36 , other program modules  37 , and program data  38 . A user may enter commands and information into the personal computer  20  through input devices such as a keyboard  40  and pointing device  42 . Of particular significance to the present invention, a camera  55  (such as a digital/electronic still or video camera, or film/photographic scanner) capable of capturing a sequence of images  56  can also be included as an input device to the personal computer  20 . The images  56  are input into the computer  20  via an appropriate camera interface  57 . This interface  57  is connected to the system bus  23 , thereby allowing the images to be routed to and stored in the RAM  25 , or one of the other data storage devices associated with the computer  20 . However, it is noted that image data can be input into the computer  20  from any of the aforementioned computer-readable media as well, without requiring the use of the camera  55 . Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  21  through a serial port interface  46  that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port or a universal serial bus (USB). A monitor  47  or other type of display device is also connected to the system bus  23  via an interface, such as a video adapter  48 . In addition to the monitor, personal computers typically include other peripheral output devices (not shown), such as speakers and printers. 
     The personal computer  20  may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer  49 . The remote computer  49  may be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the personal computer  20 , although only a memory storage device  50  has been illustrated in FIG.  1 . The logical connections depicted in FIG. 1 include a local area network (LAN)  51  and a wide area network (WAN)  52 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the personal computer  20  is connected to the local network  51  through a network interface or adapter  53 . When used in a WAN networking environment, the personal computer  20  typically includes a modem  54  or other means for establishing communications over the wide area network  52 , such as the Internet. The modem  54 , which may be internal or external, is connected to the system bus  23  via the serial port interface  46 . In a networked environment, program modules depicted relative to the personal computer  20 , or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     The exemplary operating environment having now been discussed, the remaining parts of this description section will be devoted to a description of the program modules embodying the invention. 
     1.0 Pose-Adaptive Face Detection System And Process 
     Generally, the pose-adaptive face detection process according to the present invention is accomplished via the following process actions, as shown in the high-level flow diagram of FIG.  2 . The pose-adaptive face detection system and process must first be trained before it can detect face regions in an input image. This training phase generally involves first capturing images of the faces of a plurality of people (process action  200 ). As will be explained later, the captured face images will be used to train a series of Support Vector Machines (SVMs). Each SVM will be dedicated to a particular face pose, or more precisely a pose range. Accordingly, the captured face images should depict people having a variety of face poses. Only those face images depicting a person with face pose that falls within the particular pose range of a SVM will be used to train that SVM. In addition, the more face images of a particular face pose range that are used to train a SVM, the better it will ultimately be at detecting a face region in an input image exhibiting the same pose range, including those that do not depict the faces of people used as subjects for the training images. This is true up to a saturation limit where no further improvement in detecting capability is obtained. Thus, a sufficient number of training face images should be captured at each face pose range to ensure the various SVMs are trained to the desired level of detecting accuracy. It is believed that a SVM can be adequately trained by using anywhere from about 200 to 5000 different face images having face poses within the same range. In tested embodiments of the present invention, 1000 face images were employed to train a SVM with satisfactory results. It is also noted that the more diverse the training face images are, the more accurate the detecting capability of the SVM will become. Thus, it is preferred that the face images depict people who are not generally too similar in appearance. For example, capturing faces of people of different races, sexes and ages would be advantageous to ensure the best SVM accuracy possible is achieved. The training images can be captured in a variety of ways. One preferred method would involve positioning a subject in front of a video camera and capturing images (i.e., video frames) as the subject moves his or her head in a prescribed manner. This prescribed manner would ensure that multiple images of all the different face pose positions it is desired to identify with the present system are obtained. 
     All extracted face regions from the training images are preprocessed to prepare them for eventual input into the appropriate SVM (process action  202 ). In general, this will involve normalizing, cropping, categorizing and finally abstracting the face images. The prepared face image representations are next used to train a plurality of SVMs to generally indicate how closely a region extracted from an input image depicts a person&#39;s face exhibiting a face pose falling within the face pose range associated with that SVM (process action  204 ). In process action  206 , the outputs from the SVMs are used to train a neural network to specifically identify whether a region extracted from an input image depicts a person&#39;s face, and if so to characterize the person&#39;s face pose. The system is then ready to accept prepared input face images for detection purposes and pose identification. To this end, the next process action  208  sequentially selects regions from an input image for processing. Each selected region is then abstracted in a way similar to the training images (process action  210 ) and input into the SVMs (process action  212 ). Finally, as indicated by process action  214 , the output of the SVM/neural network is interpreted to determine whether the selected region depicts a face and if so the pose range into which it falls. 
     1.1 Preprocessing Image Regions 
     As mentioned above, the preprocessing action of the pose-adaptive face detection system involves normalizing, cropping, categorizing and abstracting the training images. Specifically, referring to FIG. 3, the preprocessing preferably entails normalizing the training images to a prescribed scale (processing action  300 ). Normalizing the training images entails normalizing the scale of the images. One conventional way of accomplished this task would be to detect the location of the person&#39;s eyes in the image and to compute the separation between these locations. The image would then be scaled based on a ratio between the computed eye separation distance and a prescribed “normal” eye separation. It is noted that this action could be skipped if the images are captured at the desired scale thus eliminating the need for resizing. The desired scale for the face images is approximately the size of the smallest face region expected to be found in the input images that are to be searched. In a tested embodiment of the present invention, an image size of about 20 by 20 pixels was used with success. The image could additionally be normalized in regards to the eye locations within the image. In other words, each image would be adjusted so that the eye locations fell within a prescribed area. These normalization actions are performed so that each of the training images generally match as to orientation and size. The images are also preferably cropped (process action step  302 ) to eliminate unneeded portions that could contribute to noise in the upcoming abstraction process. One way of performing the cropping is as follows. Essentially, the midpoint between the detected eye locations is calculated and any pixels outside a box surrounding the calculated midpoint are eliminating (i.e., the intensity is zeroed). It is noted that the distance from the detected midpoint to the left side of the box, and from the midpoint to the right side of the box, will vary depending on the face pose. For example, for frontal poses, the distance between the detected midpoint and the left and right side of the box will be the same. However, if the face pose is rotated from a frontal position, the distance from the midpoint to the left side would be different than from the midpoint to the right side as needed to ensure that the background and hair is not included in the cropped image. In addition, the corner areas of the box are eliminated to omit extraneous pixels depicting the background or hair from the resulting training face image (process action step  304 ). It is noted that the extracted regions could be cropped first and then normalized, if desired. It is also noted that a histogram equalization, or similar procedure, could be employed to reduce the effects of illumination differences in the image that could introduce noise into the modeling process. 
     The next process action  306  of the training image preprocessing involves categorizing the normalized and cropped images according to their pose. One preferred way of accomplishing this action is to group the images into a set of pose ranges. For example, in the tested embodiment to be described later, the images were assigned to one of seven ranges based on the pose yaw angle (i.e., −35° to −25°, −25° to −15°, −15° to −5°, −5° to +5°, etc.). It is noted that while the tested embodiment involved a specific example where only the pose yaw angle was varied between training images (while the pitch and roll angle were set at 0°), this need not be the case. Rather, the persons in the training images could be depicted with any combination of pitch, roll and yaw angles, as long as at least a portion of their face is visible. In such a case, the normalized and cropped images would be categorized into pose ranges defined by all three directional angles. The size of these pose ranges will depend on the application and the accuracy desired, but can be readily determined and optimized via conventional means. An abstraction procedure is then used to represent the images in a simpler form to reduce the processing necessary (process action  308 ). Finally a set of coefficient (or feature) vectors is produced for each training image (process action  310 ). Each vector is tailored for input into a specific SVM as will be discussed in detail later. 
     The aforementioned abstracting procedure is essentially a method of representing the images in a simpler form to reduce the processing load associated with the SVMs detection operation. This abstraction process is further detailed in FIGS. 4A and 4B. While many abstraction processes might be employed for this purpose (e.g., histograming, Hausdorff distance, geometric hashing, active blobs, eigenface representations, and others), the preferred method entails the use of wavelet transforms, and particularly the use of three types of non-standard Haar wavelet transforms to represent each normalized, cropped, and categorized training face image. 
     As discussed in Oren [5], an image representation which captures the relationship between average intensities of neighboring image regions is produced using a family of basis functions, specifically Haar wavelets, which encode such relationships along different orientations. To this end, three types of 2-dimensional Haar wavelets are employed. These types include basis functions which capture change in intensity along the horizontal direction, the vertical direction and the diagonals (or corners). Referring to FIG. 4A, the abstraction process entails selecting one of the normalized, cropped and categorized training images (process action  400 ). A first set of image coefficients is then computed from the selected training image using a Haar wavelet procedure which encodes the relationship between average intensities of the pixels of neighboring image regions to a 4×4 scale in the vertical direction (process action  402 ). A second set of image coefficients is then computed from the selected training image using a Haar wavelet procedure designed to produce coefficients at the 4×4 scale in the horizontal direction (process action  404 ). A third set of image coefficients is also computed from the selected image, this time using a Haar wavelet procedure designed to produce coefficients at the 4×4 scale in a diagonal direction (process action  406 ). This process of computing sets of image coefficients is then repeated using the same three Haar wavelet procedures (i.e., vertical, horizontal and diagonal) except this time at a 2×2 scale (process  408  to  412 ). It is then determined if the last training image has been selected (process action  414 ). If not, process actions  400  through  412  are repeated for each remaining training images. 
     The use of the above-described wavelet transforms has several advantages. For example, both the training images used to train the system and the input images it is desired to search for faces, can be simple gray scale images. In addition, it is believed that the wavelet transforms result in a very fast and accurate detection process. The result of the wavelet transform process is a series of coefficients. 
     For each face pose range, a particular sub-set of the average coefficients from each training image are selected to form a so-called feature vector. The same number of average coefficients from each training image is preferably used to make up each feature vector. The feature vector associated with each face pose range will be made up of a different combination of the average coefficients. Thus, each training image will be represented by a unique set of the computed feature vectors—one tailored for each face pose range. 
     Referring to FIG. 4B, this tailoring process begins by calculating the mean coefficients of all the training images. To this end, all the training images depicting a person exhibiting a face pose within a particular pose range are selected (process action  418 ). The mean of all the horizontal wavelet coefficients associated with a particular pixel location of the selected training images that were computed under the first scale (e.g., 4×4), and the mean of all the horizontal wavelet coefficients associated with the pixel location that were computed under the other scale (e.g., 2×2), are calculated (process action  420 ). It is noted that the normalizing and cropping steps described previously will create training images having the same size, and so the same number of corresponding pixel locations. Process action  420  is then repeated for each pixel location of the images as indicated by process action  422 . This process of computing the means of the wavelet coefficients associate with both scales for each pixel location is then repeated for the vertical wavelet coefficients (process actions  424  and  426 ) and the diagonal wavelet coefficients (process actions  428  and  430 ). Thus, once all the coefficient means have been computed, there will be two average horizontal coefficients (i.e., one for each scale), as well as two average vertical and two average diagonal coefficients, associated with each pixel location of the training images. Those mean coefficients that have values outside a prescribed coefficient range are identified as indicated in process action  432 . The pixel location and pedigree (i.e., direction and scale) of the identified mean coefficients are then designated as the coefficients that will be used to form a feature vector for the particular pose range associated with the selected training images (process action  434 ). For example, suppose a mean horizontal wavelet coefficient computed under the 4×4 scale, which is associated with a pixel of the frontal pose range training images that depicts an area to the side of the nose of the faces, falls outside the prescribed coefficient range. In that case, whenever a feature vector is generated that is to be associated with the frontal pose range, it will include the horizontal wavelet coefficient which was computed under the 4×4 scale for the pixel at the aforementioned nose region pixel location. The foregoing selection process is repeated for the training images depicting faces exhibiting each of the remaining pose ranges as indicated by process action  436 . In this way a specific group of wavelet coefficients, as would be derived from any image undergoing the previously-described abstraction process, are identified for inclusion in a feature vector representing each of the pose ranges. 
     A preferred coefficient range that can be used to determine which of the mean wavelet coefficients will define one of the feature vector elements for a particular pose range was determined by analyzing the response profiles of the wavelet transformed training images. It was observed that there was a strong response (i.e., a distinct change in intensity in comparison to neighboring pixels) in the coefficients associated with pixels to the sides of the nose, mouth and eyes and so had a relatively large average coefficient value, whereas a weak response was observed in the coefficients associated with pixels along the cheek and forehead areas of the face and so had a relatively low average coefficient value. In contrast, the average coefficient values derived from pixels of non-face images tended to be in-between the values associated with the aforementioned strong and weak responses typifying a face image. This occurs because the corresponding pixels in non-face images are random in intensity and did not share any common pattern. This dichotomy was exploited to define the aforementioned preferred coefficient range. Essentially average coefficient values of pixel locations that either fall above or below the defined coefficient range would be considered as indicative of the distinguishing features of a face exhibiting a pose within a particular pose range, whereas average coefficient values of pixel locations falling within the coefficient range where deemed too ambiguous and so not reliable as an indicator of the distinguishing features of the face. In tested embodiments of the pose-adaptive face detection system and process, it was determined that average coefficient values which exceeded an upper threshold of between about 1.4 to 1.6, and those below a lower threshold of between about 0.4 to 0.6 were indicative of facial features, while those in between these thresholds were to ambiguous to be considered as indicative of facial features. Here the upper and lower threshold is given without coefficients normalization. Thus, as described above, only those mean coefficients that have values outside the prescribed coefficient range are identified and used to define the pixel location and pedigree of the coefficients that will be used to form a feature vector for a particular pose range. For example, in the aforementioned tested embodiments, the foregoing selection process resulted in the selection of 48 wavelet coefficient locations/pedigrees under the 2×2 scale and 8 under the 4×4 scale to be used to define the elements of a feature vector representing the image of a face within a defined frontal pose range (of −5° to +5° yaw, with 0° pitch and roll). 
     2.0 Training the SVMs and Neural Network to Detect Faces and Identify Face Poses 
     Referring to FIG. 5, the prepared face image representations are used to train a 2-stage classifier which includes a bank of SVMs,  500 , as an initial pre-classifier layer, and a neural network,  502 , forming a subsequent decision classifier layer. 
     As will be discussed in greater detail later, a region of a training image (or region of an input image)  504  is input into a wavelet-based feature generator  506  that generates coefficients using the previously discussed Haar wavelet method. Coefficients generated by this wavelet method are in turn selected to make feature vectors FV 1 , FV 2 , FV 3  and so on, which are tailored for each pose range (and so each SVM) as discussed previously. The feature vectors are then input into the respective SVMs  500  whose output is fed to the neural net  502  yielding an indication or results  508  which are interpreted to determine whether the input image contains a face, and if so identifying the face pose range of the detected face. 
     The bank of SVMs  500  is composed of a plurality of SVMs each of which is trained to detect faces exhibiting a particular range of poses. To this end, the output of each SVM would indicate whether an input image region is a face having a pose within the SVM&#39;s range, or it is not. While this output could be binary (i.e., yes or no), it is preferred that a real-value output be produced. Essentially, the real-value output of each SVM pre-classifier is indicative of the distance of an input feature vector associated an input image region from a decision hyperplane defined by the face images used to train the SVM. In other words, the output indicates how closely an input feature vector fits into the face class represented by the SVM. The number of input units or nodes of each SVM equals the number of elements making up each feature vector fed into it. This is because the vector elements are input into respective ones of these input units. Each SVM has only one output as indicated previously. Ideally, each SVM would be configured such that an image region that is not a face exhibiting a pose range associated with the SVM, or not depicting a face at all, is indicated by a negative output value. If this were the case, the face detection and pose information could be derived directly from the SVM outputs. However, this ideal is hard to achieve, especially for input regions depicting a face just outside the pose range of an SVM. In reality, such image regions may produce relatively low, but positive output values. Thus, a definitive indication of the pose range could not be made easily. This is where the second stage neural network  502  comes into play. The single neural network forming the second stage of the face detection system architecture acts as a “fusing” neural network that combines or fuses the outputs from each of the first stage SVMs. Whereas one SVM alone cannot provide a definitive indication of the face pose range of an input face region, collectively, they can when all their outputs are considered via the fusing inherent in a neural network. Structurally, the output from each of the SVMs is fed into each input of the neural network. The number of input units of the neural network is therefore equal to the number of SVMs. The number of outputs of the neural network equals the number of input units, plus at least one more. The extra output(s) is used to indicate that an input image region does not depict a face of any pose the system has been trained to detect. Thus, since the number of outputs of the neural network exceeds the number of SVMs in the system, there will be enough to allow a separate output to represent each detected face at a particular one of the pose range groups and at least one extra to indicate no face was detected. As a consequence, it can be advantageous for the output layer of the fusing neural network to include a competition sub-layer that allows only one output node to be is active in response to the input of a feature vector into the SVMs. In this way a single output node will be made active, which will represent that the face is depicted in the region of the input image under consideration and that that face exhibits a pose within a particular pose range. Thus, an output vector of a system having three SVMs might look like [1, 0, 0, 0] where the first element represent the active node. Alternately, the output could be a two element vector where the first element indicates whether a face was detected or not, and the magnitude of the second element indicates the pose range. Finally, it is noted that the number of hidden layer units or nodes in the neural network is determined empirically. 
     As indicated previously, the system must be trained before it can attempt to detect faces and identified face poses in an input image. As shown in FIGS. 6A and 6B, the SVMs are trained individually first, and then the neural network is trained using the outputs of the SVMs. As shown in FIG. 6A, in process action  600 , a SVM is selected. The selected SVM is trained by sequentially inputting the feature vectors derived from the training images exhibiting faces in the same pose range category to which the SVM is to dedicated and which have been tailored to that range (process actions  602 ). As usual the corresponding elements of each feature vector are input into the same input nodes of the SVM. Interspersed with these face image feature vectors are so-called negative examples that the SVM is instructed are not face images. A negative example is a feature vector created in the same way as the face image feature vectors, except that the image used is not of a face. The images associated with the negative example vectors preferably depict “natural” scenes not containing faces. Thus, in process action  604 , negative example feature vectors are periodically input into the selected SVM between inputs of the training image feature vectors that were derived using the same image coefficients chosen to create the training image feature vectors for the pose range to which the selected SVM has been dedicated. The selected SVM is instructed that this negative input is not a face. However, a problem can arise in that there is no typical example of the negative class. In other words, the number of different scenes not depicting a face are nearly infinite. To overcome this problem, a “bootstrapping” technique is employed. First, as indicated in process action  606 , the aforementioned training image feature vectors and negative example vectors are input into the SVM repeatedly until the output of the SVM stabilizes (i.e., does not vary outside a prescribed threshold between training iterations for each corresponding inputted vector Bootstrapping comes into play by introducing face images that have poses that fall outside the designated range of the SVM being trained once the outputs have stabilized. The feature vectors produced from these images are fed into the SVM without any indication that they are out of range. Thus, in process action  608 , whenever it is determined one of these feature vectors derived from an “out-of-range” face image results in an output that indicates the face image is within the pose range associated with the SVM in process action  610  (i.e., a false alarm), the SVM is instructed that the input is out of range (process action  612 ). Such bootstrapping results in a more accurate SVM. The foregoing training procedure is repeated for each of the SVMs as indicated in process action  614 . 
     A As shown in FIG. 6B, once all the SVMs have been trained, the neural network is brought on-line (process action  616 ) and the set of feature vectors associated with each respective training image is, in turn, simultaneously input into the appropriate SVM (process action  618 ) based on the pose range associated with the feature vector. This is repeated until the outputs of the neural network stabilize (process action  620 ). The sequence in which the feature vectors sets are input can be any desired. However, it is believed that inputting the vector sets in random pose range order will cause the neural network to stabilize more quickly. Finally, at least one face image feature vector set representing each pose range is, in turn, simultaneously input into the appropriate SVM, and the active output of the neural network is assigned as corresponding to a face being detected in an input image region and having the pose range associated with the training image used to create the feature vector causing the output (process action step  622 ). The remaining neural network outputs (which will number at least one) are assigned as corresponding to a face not being detected in an input image region. 
     3.0 Using the Pose Adaptive Detection System to Detect Faces and Identify Face Poses 
     The system is now ready to accept prepared input image regions, and to indicate if the region depicts a face, as well as indicating the pose range exhibited by the face. Referring to FIG. 7, the input image being searched is divided into regions (process action  702 ). For example, a moving window approach can be taken where a window of a prescribed size is moved across the image, and at prescribed intervals, all the pixels within the window become the next image region to be tested for a face. 
     Each input image region extracted in the foregoing manner from the input image is abstracted in a way similar to the training images (process action  704 ), to produce a set of feature vectors which are, in turn, simultaneously input into the appropriate SVMs (process action  706 ). Specifically, for each input image region, a group of image coefficients are generated using the previously described Haar wavelets for the horizontal, vertical and diagonal directions at two different scales (e.g., 4×4 and 2×2). As discussed previously, certain ones of the image coefficients are selected for each pose range. The selected coefficients are then used to form a feature vector associated with a particular pose range. In this case, feature vectors corresponding to each pose range are formed from the image coefficients derived from the input image region. Thus, the abstraction procedure results in a set of feature vectors (i.e., one for each pose range group) for each input image region. These feature vectors are input into the SVMs as indicated above. To accomplish this, each feature vector associated with an input image region is simultaneously input into the SVM that is dedicated to the same pose range as the feature vector. For each feature vector set input, an output is produced from the neural network which will have just one active node. The active node is interpreted to ascertain first whether the region under consideration depicts a face, and secondly, if a face is present, into what pose range the pose of the face falls (process action  708 ). 
     However, it is not known what size a face depicted in an input image may be, and so the size of the aforementioned window used to define each input image region must be considered. One way of ensuring that a face of any practical size depicted in an input image is captured in the window is to adopt an image pyramid approach. In this approach the window size is selected so as to be the smallest practical. In other words, the window size is chosen to be the size of the smallest detectable face in an input image. This window size should also match the size chosen for the training face images used to train the system. For a tested embodiment of the present face detection system and process, a window size of 20 by 20 pixels was chosen. Of course, many or all of the faces depicted in an input image will likely be larger than the aforementioned window size. Thus, the window would only cover a portion of the larger faces and detection would be unlikely. This is solved by not only searching the original input image with the search window (in order to find the “smallest” faces), but by also searching a series of reduced scale versions of the original input image. For example, the original image can be reduced in scale in a stepwise fashion all the way done to the size of the search window itself, if desired. After each reduction in scale, the resulting image would be searched with the search window. In this way, larger faces in the original image would be made smaller and will eventually reach a size that fits into the search window. To this end, the input image size is reduced by a prescribed scale increment (process action  710 ). This scale increment is chosen so that it is not so large that faces are missed, but not so small that the processing of the image regions is unnecessarily increased. It is then determined in process action  712  whether a reduction limit has been reached. Any desired limit could be chosen and would preferably be based on the largest face expected to be depicted in an input image. The limit could be when the image is reduced to the size of the search window itself. However, this may result in unnecessary processing if it is not believed a face will take up the entire input image. If the reduction limit has not been reached process actions  702  through  710  are repeated. This continues until the limit is reached. 
     In an alternate embodiment of the foregoing search procedure, instead of using the input image (or scaled down versions thereof) directly and then abstracting each extracted region, the entire input image (or scaled down versions thereof) could be abstracted first and then each extracted region could be feed directly into the SVMs. It is believed that this alternate procedure could increase the efficiency of the face detection system in some applications. 
     As an example of the foregoing process, suppose a region of one of the scaled input images depicts a full frontal view (i.e., pitch, yaw and roll are all 0 degrees) of the face of a subject. The image region is first preprocessed for entry into the SVMs and transformed into a series of feature vectors in the manner described previously. The feature vectors are simultaneously and respectively input into the appropriate SVMs. Suppose there are three SVMs—one dedicated to a frontal pose range of −5° to 5°, the second dedicated to a pose range of −5° to −15°, and the third dedicated to a pose range of 5° to 15°. Also, suppose the raw output from the SVMs forms the vector [0.5, −0.3, 0.1]. This would indicate the face pose is highly frontal, decidedly not close to the −5° to −15° range, but very close to the 5° to 15° range. These values would be fed into each input of the neural network. In the present example, this should result in an output vector from the neural network of [1, 0, 0, 0] where the first element has been assigned to a detected face region having a pose within the frontal pose range. Note that the last element in the neural network output vector corresponds to the negative class. Now suppose the input image region did not depict a face with a pose that the system has been trained to detect, which includes the case where the region does depict a face but not one detectable at the current scale. The output vector of the SVMs might be [−0.1, −0.2, −0.5]. In that case, the output vector generated by the neural network should be [0, 0, 0, 1], where the last element indicates the image region does not contain a face or at least not a face having a pose that falls within a pose range that the system has been trained to detect. 
     It should be noted that while the foregoing description specifically described a system and process for detecting faces, the present invention is not limited to just that embodiment. Rather, the detection system could be trained to detect objects in an image other than a face using the same methods. 
     References 
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