Стратегии детекции в рампознавании лиц / Detection Strategies For Face Recognition Using Learning And Evolution Phd Dissertation 1998

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People can easily recognize familiar human faces. Automated face recognition, however, is a difficult problem since face images can vary considerably in terms of facial expressions, age, image quality and photometry, geometry, occlusion, and disguise (Samal and Iyengar, 1992; Chellapa et. al., 1995). Face recognition starts with the detection of face patterns, proceeds by normalizing the face images to account for geometrical and illumination changes, possibly using information about the facial landmarks, identifies the faces using appropriate classification algorithms, and post processes the results using model-based schemes and logistic feedback. The variety of methods published in the literature show that there is not a unique or generic solution to the face recognition problem. Consequently, a taxonomy of face recognition technology cannot identify clear cut algorithms, but rather processing strategies describing (i) face detection and normalization, (ii) feature extraction, (iii) coding and internal representation, and (iv) classification and recognition.

Face recognition usually starts with (the location and) detection of face patterns. The reason for face detection is to focus computational resources on the face area (and thus speed up further processing), and to provide the spatial context for the normalization stage.

The objective of any face detection system is to segment the face (foreground) from its background and provide its 'box' boundary. Most face recognition systems assume that the face box is already available. Amongst those trying to detect the face box, Yang and Huang (1993) have developed a face detection system for complex backgrounds using a hierarchical knowledge-based method. The accuracy reported is 83% on a database of 60 subjects. Burel and Carel (1994) use a multi-resolution detection and localization phase consisting of scanning each image at seven scales. For each scale, the window contents are normalized to a standard size, and then propagated through a Multi Layer Perceptron (MLP). The accuracy reported is 85% on a database of 40 subjects. Sung and Poggio (1994) introduce view-based approach for face detection. In their view-based approach, faces are treated as a class of local target patterns to be detected in an image. They define a stable “ canonical” face model and generate a multidimensional Gaussian cluster with the local data distribution around centroid as pattern prototype for the purpose of face matching. 4150 normalized canonical face patterns are used to synthesize 6 “ face” pattern prototypes in the multi-dimensional image vector space and 149 face patterns contained in 23 images are tested. The system achieves a 79.9% detection rate. As another example, Rowley et. al. (1995) use a cluster of back propagation networks for face detection. A (retinal) connected neural network examines small windows of an image, and decides whether

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contains 1,199 individuals and 365 duplicate sets taken at different times and possibly wearing glasses - consisting of several poses. Since large amounts of images were acquired during different photo sessions, the lighting conditions and the size of the facial images can vary. The diversity of the FERET database is across gender, race, and age and includes duplicates as well. Fig. 4.2 is indicative of the range of facial images the FERET database consists now of.

Figure 4.2. Facial Images from FERET

Acquisition of duplicate sets is very important if one wants to assess how robust is a given face recognition system when tested on images shot at different times, which are likely to be different. Most of the sets consist of the following poses: two frontal shots ('fa' and 'fb'), 1/4 half (right and left) profiles ('qr' and 'ql'), 3/4 half (right and left) profiles ('hr' and 'hl'), and right and left (90 deg.) profiles ('pr' and 'pl'). In addition we recently collected several hundred sets that have several additional poses at the midpoints between: 'hr' and 'qr' ('ra'), 'qr' and frontal view ('rb'), frontal view and 'ql' ('rc'), 'ql' and 'hl' ('rd'), and between 'hl' and 'pl' ('re'). The additional poses were taken to assess the capability of modeling the human face using several positions and interpolating or extrapolating amongst them for future identification tasks. The facial image sets were acquired without any restrictions imposed on expression and with two or three frontal images shot at different times during the photo session. They are used as the testbed in the different experiments.

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We proposes a novel algorithm for face detection using decision trees (DT) and shows its generality and feasibility

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using a data base consisting of 2,340 face images from the FERET data base (corresponding to 817 subjects and including 190 sets of duplicates) over a semi uniform background. The algorithm decides first if a face is present, and if the face is present it crops ('box') the face. Experiments were also performed to assess the accuracy of the algorithm in rejecting images where no face is present using a small database of 25 images of various but complex background.

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The location stage indicates where is most likely to find the face contents and thus focuses attention for the next stage, that of (face) cropping. The whole procedure is straightforward and yields a rough approximation for the sought after face box.

Histogram equalization makes the image's cumulative histogram approximately linear and accounts for illumination differences as experienced during the image acquisition process. Edge detection finds transitions ('edges') in the intensity image using the Marr-Hildreth operator (Hildreth and Marr, 1980). The location stage proceeds now with horizontal and vertical projections along the x - and y - axes, respectively. The resulting profiles are searched for their maximum and minimum values using model-based ('face') constraints. The horizontal projection profile is searched for its extrema points by first detecting all its local extrema (min and max) points, and then measuring the distance d between each min and the following max peak. The top side of the boundary box is placed at the max peak location yielding the largest d distance. A similar procedure is used to analyze the vertical projection and yields the left and right sides of the boundary box. BOX1 is finally completed on its bottom side such that the ratio between the vertical and horizontal dimensions of the box is 1.5. Note that this procedure can process faces of different sizes.

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The cropping stage refines the output coming from the location stage using DT. The DT are learned ('induced') from both positive ('face') and negative ('non-face') examples expressed in terms of extracted features such as entropy, mean, and standard deviation (sd). The detection stage is run across 8x8 windows from BOX1.

Each 8x8 window and its corresponding four quadrants is processed to yield the features required for learning from examples and the derivation of the DT. Entropy, mean, and standard deviation features are derived using original image data or Laplacian processed image data from the face box to yield thirty (30=5x3x2) features values.

Once features values become available learning from examples will induce DT. As inductive learning requires symbolic data, each one of the positive examples corresponding to the face region is tagged ‘CORRECT’, while the negative or counter-positive examples corresponding to non-face regions are tagged as ‘INCORRECT’. Decision trees are induced using C4.5 (Quinlan, 1986). The input to the C4.5 consists of a string of learning events, each event given as a vector of 30 attribute values. C4.5 takes a set of positive examples and a set of negative examples and builds a classifier as a decision tree whose structure consists of

∙leaves, indicating class identity, or

∙decision nodes that specify some test to be carried out on a single attribute value, with one branch for each possible outcome of the test.

A decision tree is now used to classify each 8x8 non-overlapping window from BOX1 by starting at the root of

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One comment regarding computation is of interest. The first stage, that of (face) location, serves the role of an attention mechanism, and thus it focuses processing for later stages. The timings for the location and cropping stage are 7.9 seconds and 152 seconds per image, respectively, using SUN SPARC-20 with 50 MHz processor. When the location stage is not used and the cropping stage takes place over the whole image, the time required is 288 seconds per image. On the average, the use of the location ('attention') stage thus reduces computation by 45%, while the corresponding space search is reduced by 53%.

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To explore the extent to which color information is helpful with face location, we have performed two experiments for locating the face using (i) color and (ii) gray scale images, respectively. Both the color and its corresponding gray scale data set consist of 200 images at a resolution of 64x96. Among the 200 images 20 images are used to generate training examples while the remaining 180 images are used to generate test cases. The approach used for face location involves two stages: (i) windows classification and (ii) post processing. The first stage labels 4x4 windows as face or non-face cases using decision trees (DT). The DT for gray scale images are induced ('learned') from both positive ('face') and negative ('non-face') examples expressed in terms of 12 features such as entropy and mean with / without Laplacian preprocessing. The same types of features are used for both gray scale and color images. As color images consist of R, G, and B channels, 36 rather than 12 features are computed for each color window. The labeled output from the first stage is post processed using horizontal and vertical projections to locate the boundaries defining the face boxes. The projections count the number of 4x4 windows labeled as face cases.

For the color data set, we normalize the color values from the RGB space to rgb space, where

Each 4x4 window and its corresponding four 2x2 quadrants are processed to yield the features required for learning from examples and the derivation of the DT. Entropy and mean features are derived using original image data or Laplacian preprocessed image data from each 4x4 window to yield 36 features and 12 feature vales for color and gray scale images, respectively. Once the features values become available learning from examples will induce the DT. A decision tree will then be used to classify each 4x4 non-overlapping window from test images.

Experiments were run on a total of 200 face images corresponding to 200 subjects with a resolution of 64x96 encoded using 256 gray scale levels for gray-scale images, and using the r, g, b normalized color components for color images. A sample set of face images is shown below in Fig. 4.6. The 200 images were divided into sets of 20 and 180 images for training and testing, respectively. From each of the 20 training images, a total of 384 windows of dimension 4x4 (some boxes corresponding to the face and others corresponding to the non-face regions) were manually labeled. 7680 windows (+/- examples) consisting of 36 (from color images) or 12 (from gray scale images) feature values for each window are used to train the DT.

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Figure 4.6. Gray Scale vs. r, g, b channels using Color Images

The accuracy rate on face location is based on visual inspection to determine if the box obtained includes both eyes, nose, and mouth, and that the top side of the box is below the hairline. The overall accuracy rate is 85.5% for gray scale images and 90.6% for color images. Examples of box faces are shown in Fig. 4.7.

Figure 4.7. Examples of Face Detection results

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As more and more forensic information becomes available on video some effort of face detection has been done for the Automatic Video-Based Biometric Person Authentication (AVBPA) in this thesis. For an AVBPA system, possible tasks and application scenarios under consideration involve detection and tracking of humans and human (ID) verification. Authentication corresponds to ID verification and involves actual (face) recognition for the subject(s) detected in the video sequence. As it is described below the architecture for AVBPA takes advantage of the active vision paradigm and it involves difference methods or optical flow analysis to detect the moving subject, projection analysis and decision trees (DT) for face location, and connectionist network - Radial Basis Function (RBF) for authentication.

Active Vision (AV) has advanced the widely held belief that intelligent data collection rather than image recovery and reconstruction is the goal of perception. Active vision, also referred to as Active, Purposive and Selective Perception (APSP), involves a large degree of adaptation, and provides an intelligent (video) observer (system) with the capability to decide where to seek information, what information to pick up, and how to process it.

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