Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin, South Korea Copyright © solarlits.com

Introduction Objective Overview of proposed method Eigenface recognition in clutter Background representation Classifier Proposed method Experimental results Conclusions

Face recognition is quite a difficult task because faces are a natural class of complex, multidimensional objects. Fisher’s linear discriminant (FLD) and Eigenface recognition (EFR) methods are quit well when input test patterns is a face. EFR If the threshold is set high, it ends up missing If the threshold is lowered to capture the face, it gives many false alarms It is quite sensitive to the choice of the threshold value.

Good face recognition system Detect and recognize all faces in a scene Not missclassify background patterns as faces Precautions Few false alarms will render the system ineffective performance should not be too sensitive to any threshold selection.

Distance from eigenface space (DFFS) and distance in eigenface space (DIFS) are suggested to detect and eliminate nonfaces for robust face recognition in clutter. We show that these are not sufficient to discriminate against arbitrary background patterns in the absence of any information about the background.

To handle clutter in still images requires Good face detection module to find face patterns And feed only those patterns as input to traditional EFR scheme

Within Principal component analysis (PCA) to robustly recognize faces in the presence of clutter. Traditional eigenface recognition (EFR) method, which is based on PCA, works quite well when the input test patterns are faces. But poor when recognize faces appearing against a background It May miss faces or may wrongly associate many background image patterns to faces To remove this, learning the distribution of the background is helpful

1.Construct an “eigenbackground space” which represents the distribution of the background images corresponding to the given test image. 2.The background is learned “on the fly” 3.Provides a sound basis for eliminating false alarms. 4.An appropriate pattern classifier is derived 5.Eigenbackground space together with the eigenface space is used to simultaneously detect and recognize faces.

Tranning set of face images Mean where L is the total number of training images PCA solves for a set of L orthogonal vectors where Is maximum subject to U n and λ n are the eigenvectors and eigenvalues of where

Weight vector corresponding to pattern T i The distance in face space (DIFS) is Minimum DIFS is declared as recognised face Distance from free space (DFFS) is defined as If then test pattern is nonface image

If threshold is the smallest, real faces are not missed, but many false alarms If threshold is smaller, we will miss some faces The threshold for DFFS and DIFS need to be higher, but then we get false alarms Therefore, EFR is sensitive to threshold Properties of background must be utilized to solve this issue

Nonface patterns could be confused as face patterns Learning Universal background class Background distribution local to a given test image Utilization of face and background distributions Reduce false alarms Decrease sensitivity to the choice of threshold

A window pattern in the test image is classified (positively) as a background pattern if its distance from the eigenface space is greater than a certain (high) threshold Background patterns are distributed into K clusters Few clusters result under-representation of background class Too many is not possible due to limited training samples Pattern centers are few as compared to background patterns, and are used for learning eigenbackground space

1.Eigenvectors of the covariance matrix of the set of background pattern centers. 2.The subspace spanned by the eigenvectors corresponding to the largest eigenvalues of the covariance matrix is called the eigenbackground space. 3.Eigenvectors of C b is called “eigenbackgroung images” 4.Image T is converted into eigenbackground components by

Faceclass (w 1 ) Background class (w 2 ) Weight vector

Estimator for face Reconstruction error in x

Estimator for background Reconstruction error in x X b is the estimate of x Weight

Image pattern is classified as face if is positive, else not. When, when the number of eigenfaces and eigenbackground patterns are the same, and when, i.e., when the arithmetic mean of the eigenvalues in the orthogonal subspaces is the same Reconstruction error function

A scheme that recognizes faces by searching a given test image for patches of image patterns of faces appearing against a cluttered background Stages Estimation of the eigenface space Construction of the eigenbackground space Recognition

Image recognition Eigenface space and the eigenbackground space are learnt using training images The test image is examined again in the presence of faces at all points in the image. At every pixel in the test image, a subimage is cropped about that pixel to obtain the test patterns. For each of these test window patterns, the classifier is used to determine whether a pattern is a face or not.

Let subimage pattern in the test image is It is projected onto the eigenface and eigenbackground where is the threshold The pattern is recognized as belonging to the i th person if where q is the number of face classes or people in the database

Compute eigenfaces Identify Prominent Background Images High threshold, far from eigenface space marked as background Calculate Background Pattern Centers Using K-mean algorithm, to reduce background patterns Obtain Eigenbackground Images Eigenvectors from highest eigenvalues Detect and Recognize Faces in the Scene Detection of face by classifier and DIFS

Fig. 2. Architecture of the proposed system. (a) Computation of eigenfaces. (b) Construction of eigenbackground space. (c) Face detection and recognition.

Fig. 3. (a) Test case where a person appears naturally against a cluttered scene. (b) Results for the traditional EFR technique. (c) Results using the proposed method. (d) Some of the background pattern centers returned by the K-means algorithm. (e) First eight eigenbackground images for the background local to the test image. (f) Typical eigenfaces.

Fig. 4. (a) Test images with different complex backgrounds. Results for (b) traditional EFR and (c) the proposed method.

Fig. 5. Representative results for the proposed method on some more test images.

Fig. 6. (a) Few of the test cases where the proposed method had false alarms. (b) Test cases where the person is not in the training set.

Fig. 7. Some results for the proposed method on outdoor images. (a) Examples of side-view of faces. (b) Different illumination conditions for two individuals. (c) Example images containing several people within the same image.

Fig. 8. Detection rate versus FAR the proposed method and the traditional EFR method.

when the scheme is directly extended to recognize faces in the presence of background clutter, its performance degrades as it cannot satisfactorily discriminate against nonface patterns. The background space which is created “on the fly” from the test image is shown to be very useful in distinguishing nonface patterns. The scheme gives very good results with almost no false alarms, even on fairly complicated scenes. For background learning, one must decide the number of background centers based on resolution of the image.

In order to reduce the global computational complexity of the algorithm. Instead of processing each and every pixel, one could process every alternate pixel along rows and columns One could skip processing of some of the pixels in the immediate neighborhood of an already identified face. If people appear against a relatively constant or slowly changing clutter, background learning need be done either only once or very infrequently.