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

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Figure 17: Performance of eyeglass detection with FAR and FRR Table 3 shows the performance of the eyeglass detection algorithm with two fixed thresholds (face-0.70, eyeglass –0.57) as shown in Figure 17. Correct detection when eyeglasses are worn is 86.6% and when no eyeglasses are worn is 97.1%. The parameters used in eyeglass detection are 0 .5 < α < 1. 5 , 0 .8 < β < 1. 2 , θ
Table 3: Performance for eyeglass detection Descriptions Matched /Total Images Accuracy True Positive (Eyeglass-> Eyeglass) 445/ 514 86.6% True Negative 1096/1129 97.1% (No Eyeglass -> No Eyeglass) False Positive 33/1129 2.9% (No Eyeglass -> Eyeglass) False Negative (Eyeglass ->No eyeglass) 85/514 13.4% After detecting eyeglasses, it will be better to replace the eyeglass regions with average eye templates in the thermal images to minimize the effect of blocked information. Figure 18 shows the average eye template to be replaced with eyeglasses regions and Figure 19 shows example images after eyeglasses removal in the thermal images and fused images. Here we used only one eye template but it will be better to use different types of eye templates depending on the size, shape and coverage regions of the eyeglasses. Figure 18 : The average eye regions template 36
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: Eyeglasses (b) No eyeglasses (c) Fi
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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