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

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(a) (b) (c) (d) (e) (f) Figure 14: Face detection example: (a) thermal image, (b) thresholded image, (c) after morphological operation, (d) connected components, (e) fitting ellipses, and (f) cropped face region 3.3 Eyeglass Detection and Removal from Thermal Images One of the disadvantages of using thermal images for face recognition will be the fact that eyeglasses block thermal emissions from the face area. Automated eyeglass detection is thus important in implanting further processing for recognition. Eyeglass regions are hotter than the background and cooler than any of the facial components. After detecting face regions eyeglasses can be detected in the same manner using face 31
detection algorithm as described above. Figure 15 shows classification of eyeglasses in thermal images. A thermal image is thresholded and ellipses are fitted. Based on the ellipses, each blob is processed to check similarity. In case where one blob is detected, we determine there are no eyeglasses since we are using only frontal images. Total comparison for N blobs will be N(N - 1)/2 . We computed similarity of two blobs in terms of α , which represents the ratio of major and minor axis of a ellipse, β , which represents the ratio of size (major*minor) of two ellipses, θ which represents the orientation of two blobs, γ , which represents the distance of two blobs compared to the width of the face, and κ , which represents the location of ellipses relative to the height of the face. After checking the similarity, only the best two blobs will remain for claiming the presence of eyeglasses. Figure 15: Diagram of eyeglass detection algorithm 32
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: ignored to achieve better performan
Page 41 and 42: Eyeglasses (b) No eyeglasses (c) Fi
Page 43 and 44: Table 3: Performance for eyeglass d
Page 45 and 46: 4.1 Feature Based Face Recognition
Page 47 and 48: In addition to feature based method
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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