triangle-based approach to the detection of human face
DESCRIPTION
Triangle-based approach to the detection of human face. Source : Pattern Recognition, Vol. 34, Issue 6, June 2001, pp. 1271-1284 Authors : Chiun-Hsiun Lin, Kuo-Chin Fan Speaker : Chia-Chun Wu ( 吳佳駿 ) Date : 2004/11/25. Outline. Introduction Proposed system - PowerPoint PPT PresentationTRANSCRIPT
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Triangle-based approach to the detection of human faceSourcePattern Recognition, Vol. 34, Issue 6, June 2001, pp. 1271-1284AuthorsChiun-Hsiun Lin, Kuo-Chin FanSpeakerChia-Chun Wu ()Date2004/11/25
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OutlineIntroductionProposed systemPotential face regionsFace verificationExperimentalConclusionsComments
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Introduction2 principal parts of the proposed systemPotential face regionsFace verification
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Proposed systemPotential facial regionsLabel all 4-connected components and find the center of each blockFind any 3 centers to form a triangleNormalize the size of potential facial regionsVerified by thresholdInput imageDisplay the detection result(60 * 60 pixels)
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Frontal view(a)(b)(c)(d)(e)(f)(a) is the original image(b) is the binary image(c) is the isosceles triangle formed by the 3 centers of 3 blocks(e) is the best potential facial region which is normalizedto a standard size (60 * 60 pixels)(f) is the result of the facial detection(d) is the best potential facial region cropped from the binary image that covers the isosceles triangle
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Side view(a)(b)(c)(d)(e)(f)
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Potential face regionsPixel Tblack values 1Pixel > Twhite values 0For exampleT = 128185 > 128 1
504820018553193178180515456495916517360
0011011100000110
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Potential face regionsFor exampleThe center point of Block1
The center point of Block2
- Potential face regionsThe isosceles triangle i, j, k. (i
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Potential face regionsX1 = X4 = Xi - 1/3*D(i, k), X2 = X3 = Xk + 1/3*D(i, k),Y1 = Y2 = Yi + 1/3*D(i, k), Y3 = Y4 = Yj - 1/3*D(i, k).
- Potential face regions(a) The right triangle i, j, k. (i
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Potential face regionsX1 = X4 = Xj - 1/6*D(j, k), X2 = X3 = Xj + 1.2*D(j, k),Y1 = Y2 = Yj + 1/4*D(j, k), Y3 = Y4 = Yj - 1.0*D(j, k).X1 = X4 = Xi - 1/6*D(i, j), X2 = X3 = Xi + 1.2*D(i, j),Y1 = Y2 = Yi + 1/4*D(i, j), Y3 = Y4 = Yi - 1.0*D(i, j).
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Face verificationMask is formed by 10 binary training faces. For example=Threshold = 5
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Face verificationFor all pixelsFor examplePotential facial regionmaskWeight = - 2 - 2 + 6 + 2 + 6 + 6 + 2 + 2 + 2 = 22
001011000
111011000
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Face verificationThe threshold of frontal view is 4000~5500.The threshold of side view is 2300~2600.
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ExperimentalNeeds about 28 s to locate the correct face position. (200 * 145 pixels)Needs less than 2.5 s to locate the correct face position. (200 * 307 pixels)Experimental results of gray-level images with simple/complex backgrounds
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ExperimentalExperimental results of color images with simple/complex backgrounds
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ExperimentalVerification of face images with different sizes
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ExperimentalVerification of face images with altered lighting conditions
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ExperimentalVerification of face images with distinct positions
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ExperimentalVerification of face images with changed expressions
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ExperimentalVerification of defocus face images
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ExperimentalVerification of face images (a) with noise; (b) with partial occlusion of mouth; (c) wearing sunglasses
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ExperimentalThe face of a cartoon doll; (b) The face of a Chinese doll
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Experimental (error detected)The face is too dark to be detected
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Experimental (error detected)The face with right eye being occluded by the black hair
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ConclusionsDetect multiple faces in complex backgroundsHandle different sizes, different lighting conditions, varying pose and expression, and noise and defocus problemsPartial shelter and side viewThe minimum size of a face that could be detected is 5 * 5 pixelsThe success rate is approximately 98%.
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Comments