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

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In order to see variations among the faces, we cropped each region of the average images. Figure 11 shows each feature variation among thermal face images. The eyes are relatively the hottest regions. The eyebrows, cheek, and the mouth are all almost in the same range temperature. The nose is the coldest area if individuals are not wearing eyeglasses while eyeglass regions are the coldest area if individuals are wearing eyeglasses. Figure 11: Facial feature variations of average images indicated by histograms 3.2 Face Detection from Thermal Images Ellipses are often used in face-related technology such as face tracking, face detection, head tracking and other facial component analysis. Since thermal faces are integrated with several features comprised of different blobs, the use of an ellipse can be a powerful representation of certain features around the faces in the thermal images. The general equation of a conic can be represented as 27
2 2 F(A,T)= AT = ax + bxy + cy + dx + ey + f = 0 (2) where = [a,b,c,d,e, f] 2 2 T A and T = [x ,xy, y ,x, y,1] .Commonly used conic fitting methods minimize the algebraic distance in terms of least squares N Λ 2 A= arg min{ ∑ (F(A,Ti )) } . (3) A i=1 Researchers suggested different methods to fit conics while applying constraints over Λ A , 1 2 2 2 2 such as a + c = 1, f = 1, and a + b + c = 1 . Bookstein [53] showed that if a quadratic constraint is set on the parameters, the minimization can be solved by a generalized eigenvalue system which can be denoted as D T DA= SA= λCA . (4) where T 1 ,x2 ,..,xn ] D = [x is called the design matrix, D S = D T is called the scatter matrix and C is a constant matrix. Least squares conic fitting was commonly used for fitting ellipses, but it can lead to other conics. Fitzgibbon et al [54] proposed direct least conic fitting algorithms while applying constraints A T CA= 1. (5) They introduced a non-iterative ellipse-fitting algorithm that yields the best least square ellipse fitting method. Their method has a low eccentricity bias, is affine-invariant, and is extremely robust to noise. Figure 12 shows a demo for fitting ellipses. We make use of OpenCV which provides high-level image processing libraries to check the robustness of ellipse fitting functions. The original image is processed using a user-specified threshold, then each 28
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: (a) (b) (c) Figure 9: Average therm
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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