Fusion of Visual and Thermal Face Recognition Techniques: A ...

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(a) (b) (c) Figure 21: Different landmarks; (a) 15 points, (b) 30 points, and (c) 44 points. [ images from FERET] It is apparent that the eyes, nose, and mouth regions are also useful for recognition. Humans also recognize faces using these features. We also extracted these regions based on landmark points around them for face recognition as shown in Figure 22. Here, we need to also find out how to perform optimum weighting since those regions work differently in the recognition stage. (a) (b) (c) Figure 22: Features based on face regions. (a) Original face image (256*384 pixels), (b) normalized face image (200*200), and (c) extracted eye (30*95), nose (30*40), and mouth (25*60) regions. 39
In addition to feature based methods, we use the Eigenface algorithm which is a part of holistic based face recognition algorithms as shown in Figure 23. Figure 23: Eigenface-based face recognition system A detailed description of Eigenfaces is as follows. Each face image is a column vector in a matrix. Here we use a 100*100 image size, then, the column matrix X i becomes 1*10000. Let T = [X 1 ,..., X N ] , where N is the number of faces. After normalizing each matrix with an average face ( A = X - i = 1,..., N) i i N - X i n=0 m; where ∑ 1 m = ), the covariance matrix T C = AA (10000*10000) can be calculated. Finding the eigenvectors of the matrix with 10000*10000 dimensions is an intractable task, we compute L = A T A first, then use the eigenvector equation; A T Av i = λ i v i , where λ i represents the eigenvalues and v i represents the eigenvector of L. Finally, the eigenvectors U (called Eigenfaces) can be achieved by multiplying the normalized training set A with eigenvectors generated from L using these equations; AA T Av = λ Av and U = AV(U = [u ,...u ],V = [v ,...,v ] i i i 1 M 1 M . 40
Page 1 and 2: Fusion of Visual and Thermal Face R
Page 3 and 4: Abstract This paper describes how f
Page 5 and 6: List of Tables Table 1: Datasets...
Page 7 and 8: Figure 27: Example images of same i
Page 9 and 10: heat emitted by objects. The use of
Page 11 and 12: illumination source can reduce ligh
Page 13 and 14: identification recognition systems
Page 15 and 16: The LFA constructs a family of loca
Page 17 and 18: interest in high-end security appli
Page 19 and 20: covers any area, which deals with u
Page 21 and 22: decision fusion schemes (average co
Page 23 and 24: sensors for experimentation and sta
Page 25 and 26: lights, we have two extra spot ligh
Page 27 and 28: taken with the eyeglasses off. For
Page 29 and 30: (a) (b) (c) Figure 6: A data fusion
Page 31 and 32: Edge detection methods such as Cann
Page 33 and 34: (a) (b) (c) Figure 9: Average therm
Page 35 and 36: 2 2 F(A,T)= AT = ax + bxy + cy + dx
Page 37 and 38: ignored to achieve better performan
Page 39 and 40: detection algorithm as described ab
Page 41 and 42: Eyeglasses (b) No eyeglasses (c) Fi
Page 43 and 44: Table 3: Performance for eyeglass d
Page 45: 4.1 Feature Based Face Recognition
Page 49 and 50: Figure 24: Accuracy results based o
Page 51 and 52: other factors such as compression a
Page 53 and 54: Figure 29: Pose test summary Table
Page 55 and 56: ‘Medium’ as not ambient but sti
Page 57 and 58: Based on the identification experim
Page 59 and 60: Figure 33: Decision fusion example
Page 61 and 62: in performance in this case. This i
Page 63 and 64: Figure 36: Performance comparison w
Page 65 and 66: Figure 39: Performance comparison o
Page 67 and 68: average eye template in thermal ima
Page 69 and 70: [14] P. S. Penev and J. J. Atick,
Page 71 and 72: [30] P. J. Phillips, “Support Vec
Page 73 and 74: [47] http://www.equinoxsensors.com/
Page 75: Vita Jingu Heo was born in Daegu, K

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