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

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(a) (b) (c) (d) (e) Figure 19: Data fusion after eyeglass removal; (a) original visual images, (b) original thermal images with glasses, (b) thermal images after eyeglass region replacement, (d) original fused images, and (e) fused images after eyeglass region replacement 4. Comparison of Face Recognition Techniques In this chapter, comparisons of visual face recognition have been performed with feature based methods and appearance based methods using FERET database. The results then are compared with FaceIt® which uses local feature algorithm. The performance of Visual and thermal face recognition are also evaluated using FaceIt® under different fusion methods. 37
4.1 Feature Based Face Recognition We used 610 faces (200 gallery images (fa images)), 410 subject images (fb images)) from the FERET database to evaluate analytic and feature based face recognition algorithms. We did not use all of the images provided by FERET but selected only those suitable for this experiment. The 2-letter codes (fa, fb, and etc) indicate the type of imagery. For example, fa indicates a regular frontal image. Detailed naming conventions can be seen at http://www.itl.nist.gov/iad/humanid/feret/feret_master.html. It is important to extract this distance information considering their significance in representing a face, and reliability in extracting faces automatically. In addition, it is important to weight distance information, because each works differently in the recognition process. We used geometric distances, which were extracted from 30 landmark points as shown in Figure 20, and different sets of landmarks as shown in Figure 21. Figure 20: Geometrical distances on the face [image from FERET] 38
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: Table 3: Performance for eyeglass d
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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