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

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Acknowledgements First of all, I would like to thank all IRIS Lab members. I would like to thank my advisor, Dr. Mongi Abidi. His willingness to support my work and his guidance throughout my studies has allowed me to develop my skills as a face recognition researcher within a supportive team environment. Also, I would like to thank, Dr. Besma Abidi, Dr. Seong Kong, and Dr. Joongi Paik. Their advice over the years has been of equal importance. ii
Abstract This paper describes how fusion of visual and thermal face recognition can increase the overall performance of face recognition systems. Visual face recognition systems perform relatively reliably under controlled illumination conditions. Thermal face recognition systems are advantageous for detecting disguised faces or when there is no control over illumination. Thermal images of individuals wearing eyeglasses may result in poor performance since eyeglasses block the infrared emissions around the eyes, which are important features for recognition. With taking advantages of each visual and thermal image, the new fused systems can be implemented in collaborating low-level data fusion and high-level decision fusion. This paper contains a comparative analysis of visual, thermal and fused face recognition performance and provides a breakthrough for future intelligent face recognition capable of making decisions based on visual, thermal or fused images depending on different situations. iii
Page 1: Fusion of Visual and Thermal Face R
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
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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
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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
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Figure 29: Pose test summary Table
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‘Medium’ as not ambient but sti
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Based on the identification experim
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Figure 33: Decision fusion example
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in performance in this case. This i
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Figure 36: Performance comparison w
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Figure 39: Performance comparison o
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average eye template in thermal ima
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[14] P. S. Penev and J. J. Atick,
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[30] P. J. Phillips, “Support Vec
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[47] http://www.equinoxsensors.com/
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Vita Jingu Heo was born in Daegu, K
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