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

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Histogram based automatic thresholds make it even harder to find the optimal threshold for segmenting facial components. Figure 8 shows the histograms of an original and equalized thermal image. Since the optimal threshold, which can be derived from the histogram, does not contain any regional information, the approach to solve local minima or local maximum has it own limitations. (a) 0 (b) 1 (c) 0 (d) 1 Figure 8: Problems with histogram methods (a) original thermal image, (b) histogram of (a), (c) equalized thermal image, (d) histogram of (b) Therefore, we make use of average thermal images to measure variation over thermal face images instead of observing each individual thermal face. Figure 9 shows the average of thermal images generated from 60 images without eyeglasses (a) and with eyeglasses (b). Figure 9(c) was generated from 200 images from the FERET database. All images are normalized using manual eye positioning. 25
(a) (b) (c) Figure 9: Average thermal images; (a) without eyeglasses, (b) with eyeglasses and (c) average visual image From the average thermal images of Figure 9, we measured statistical variations of the thermal images. Figure 10(b) shows the histogram of each thermal image without and with eyeglasses and (c) shows the color-coded thermal images. Skin regions are a relatively hotter area than the background and the nose or eyeglasses regions are the coldest areas in thermal images. From the histogram Figure 10(b), one can observe that it is not simple to use automatic thresholds based on the histogram itself. The maximum threshold was set to 1. 0 0.21 0.71 1 0 0.21 0.47 0.75 1 (a) (b) (c) Figure 10: Average thermal images without (top)/with (bottom) eyeglasses, gray level histograms and color-coded images 26
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: Edge detection methods such as Cann
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 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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