FACE RECOGNITION AND VIDEO FACE TRACKING Deepa ...

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Eigen faces and face recognition using wavelet sub-band methods. Chapter 5 is about detecting and tracking a face in the video. This explains the algorithm and design and program flow of the video tracking system and also shows the result of the video tracking system. Chapter 6 has the conclusion of the entire work. It also talks about future implementations. In Chapter 1, we learnt brief introduction about Face detection, Face recognition and Video tracking. Let us learn about different techniques and algorithms in the following Chapter. 3
Chapter 2 FACE RECOGNITION, DETECTION AND VIDEO TRACKING TECHNIQUES This Chapter is about different techniques that are used in recognizing the face, detecting the face and tracking the video object in general. It also briefly describes the algorithms available in the market for the above. Several algorithms are used for face recognition. Some of the popular methods are discussed here. Face recognition by feature matching is one such method [8]. We have to locate points in the face image with high information content. We don‟t have to consider the face contour or the hair. We have to concentrate on the center of the face area, as most stable and informative features are found there. The high informative points in the face are considered around eyes, nose and mouth. To enforce this we apply Gaussian weighting to the center of the face. Linear Discriminate Analysis algorithm is also used in face recognition. Originally developed in 1936 by R.A. Fisher, Discriminant Analysis is a classic method of classification that has stood the test of time [5]. Discriminant analysis often produces models whose accuracy approaches (and occasionally exceeds) more complex modern methods [5]. LDA is similar to Principal Component Analysis (PCA). In both PCA and LDA we look for the linear combinations of the variables which explain the given data well. LDA explicitly attempts to model the difference between the classes of data [8]. PCA on the other hand does not take into account any difference in class but builds the feature combinations based on differences rather than similarities. 4
Page 1 and 2: FACE RECOGNITION AND VIDEO FACE TRA
Page 3 and 4: Student: Deepa Seetharamaiah I cert
Page 5 and 6: For face detection, Haar-like featu
Page 7 and 8: TABLE OF CONTENTS Acknowledgments..
Page 9 and 10: LIST OF TABLES Table 1 - Face recog
Page 11 and 12: SOFTWARE SPECIFICATIONS Different S
Page 13: Face recognition system is an impor
Page 17 and 18: Video tracking is the process of lo
Page 19 and 20: vision and is generally a prerequis
Page 21 and 22: Certain Eigen faces that do not con
Page 23 and 24: Flow chart of the Algorithm is show
Page 25 and 26: the order of the images in the trai
Page 27 and 28: document. Select the menu “Face R
Page 29 and 30: 4.1 Introduction Chapter 4 FACE REC
Page 31 and 32: The sub-band images have a wide ran
Page 33 and 34: Then, the sub-band 4 images of all
Page 35 and 36: 4.3 Program Flow and Running the Pr
Page 37 and 38: Figure 11 - Wavelet Decomposition F
Page 39 and 40: 5.1 Introduction Chapter 5 VIDEO FA
Page 41 and 42: Haar classifer cascade profile Show
Page 43 and 44: using JMF‟s API. Convert the colo
Page 45 and 46: elow: Chapter 6 CONCLUSION This pro
Page 47 and 48: REFERENCES [1] M. A. Turk and A. P.

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