Application of Face Recognition to Person Matching in Trains

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Bernice Monica Hamilton
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1 Application of Face Recognition to Person Matching in Trains May 2008

2 Objective Matching of person Context : in trains Using face recognition and face detection algorithms With a video-surveillance camera

3 Introduction Faces : a lot of research last deceny Still one of the most active domain Successful algorithms developed. Face detection : neural networks, boosted cascade Face recognition : PCA,LDA, HMM, EBGM, AAM, LBP

4 Face detection Cascade of boosted Haar classifiers. Image scanned by variable size window window is feed to the cascade First classifiers are simple (fast). Focus on easy window. Last classifiers are complex (slow). Focus on hard window.

5 Face detection (continued) A boosted classifier (strong) = combination of T classifiers from N (N>T) simple classifiers by ADABoost boosted classifier is better than one simple classifier alone. simple classifier = simple decision tree (ex : stump) simple classifier = thresholding a value value = difference of (weighted) sums of pixels over features feature = rectangle (haar wavelet like)

6 Face detection (continued) feature = prototype + a size + a position in the window

7 Face detection (continued) cascade is trained on image of fixed size (24x24) for a small size (24x24), we can have huge number of possible features best features for face detection are selected implicitly by AdaBoost with respect to specifications (error rates) of a strong classifier.

8 Face detection (continued) To detect face of any size, the features are scaled (scalable window) Cascade on large images is computing intensive. Segment all skin regions by color then apply cascade on these regions only Skin detection by simple rules on U and V channels (YUV). Rules learned by statistics on large database of images.

9 Face detection (continued)

10 Face detection (continued)

11 Face normalization To derive relevant features of a face a normalisation is necessary. Photo normalisation : 1) histo equalization 2) hyperbolic tan 3) mean 0, var 1 Geometrical normalisation : 1) Find face left and right boundaries -> max of horiz gradients accumulated for each column. Then refine by checking skin proportion pixels around the lines. 2) Find best line passing by near the eyes -> max of horiz gradients acc. for each line. 3) crop a region with respect to eyes position (line) and face width.

12 Face normalization (continued)

13 Derive features of a face Use of texture descriptor : local binary pattern (LBP) Transform each pixel of gray image into binary pattern

14 Derive features of a face (continued) Uniform binary pattern if at most two bitwsise transitions. Account for percent of the image Non uniform pattern are ignored.

15 Match two faces Distance : For each uniform LBP, find the nearest (w.r.t position in fixed NBhood) corresponding LBP in the image to compare and accumulate the distance for each pixel. Best match by nearest neighbour classifier.

16 Matching performances

17 Future work tracking of face (by condensation) specify the context (tune the camera to focus on faces) -> more complex geometrical normalisation more complex photo normalisation. automatic generation of the database. use of LTP.

Face detection is a process of localizing and extracting the face region from the

Chapter 4 FACE NORMALIZATION 4.1 INTRODUCTION Face detection is a process of localizing and extracting the face region from the background. The detected face varies in rotation, brightness, size, etc.