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Triangle-based approach to the detection of human face

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

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Triangle-based approach to the detection of human face

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  1. 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

  2. Outline • Introduction • Proposed system • Potential face regions • Face verification • Experimental • Conclusions • Comments

  3. Introduction • 2 principal parts of the proposed system • Potential face regions • Face verification

  4. Fed into the weighting mask function Proposed system Label all 4-connected components and find the center of each block Find any 3 centers to form a triangle Input image Potential facial regions Verified by threshold Normalize the size of potential facial regions (60 * 60 pixels) Display the detection result

  5. Frontal view (e) is the best potential facial region which is normalized to a standard size (60 * 60 pixels) (d) is the best potential facial regioncropped from the binary image that covers theisoscelestriangle (c) is the isosceles triangle formed by the3 centers of 3 blocks (a) is the original image (b) is thebinary image (f) is the resultof the facialdetection (b) (c) (a) (f) (e) (d)

  6. Side view (b) (c) (a) (f) (e) (d)

  7. Binarization by threshold T Potential face regions 185 > 128  1 Pixel ≦T：black values 1 Pixel > T：white  values 0 For example： T = 128

  8. Label all 4-connected components and find the center of each block Potential face regions For example： The center point of Block1 The center point of Block2

  9. The matching rules for finding an isosceles triangle (the frontal view) Potential face regions D(i, j) = sqr((Xi-Xj)2 + (Yi-Yj)2) • The isosceles triangle i, j, k. (i<j<k) (b) Three points (i, j, and k)satisfy the matching rules, which will form an isosceles triangle. abs(D(i, j)-D(j, k))<0.25*max(D(i, j), D(j, k)), abs(D(i, j)-D(i, k))<0.25*max(D(i, j), D(j, k)).

  10. Assume that (Xi, Yi), (Xj, Yj) and (Xk, Yk) are the three center points of blocks i, j, and k, respectively. The four corner points of the face region will be (X1, Y1), (X2, Y2), (X3, Y3), and (X4, Y4). Potential face regions X1=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).

  11. The matching rules for finding a right triangle (the right/left side view) Potential face regions (a) The right triangle i, j, k.(i<j<k) (b) Three points (i, j, and k) form a right triangle (30°, 60°,90°). abs(D(i, k)-D(j, k))<0.60*D(i, k) and abs(D(i, k)-D(j, k))>0.40*D(i, k) abs(D(i, k)-D(i, j))>0.13*D(i, k) and abs(D(i, k)-D(i, j))<0.19*D(i, k) abs(D(i, j)-D(j, k))<0.44*D(i, k) and abs(D(i, j)-D(j, k))>0.29*D(i, k)

  12. Assume that (Xi, Yi), (Xj, Yj) and (Xk, Yk) are the three center points of blocks i, j, and k, respectively. (X1, Y1), (X2, Y2), (X3, Y3), and (X4, Y4) are the four corner points of the side view region. Potential face regions X1=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).

  13.  Normalized to a standard size (60*60 pixels) Feed into the weighting mask function Face verification Mask is formed by 10 binary training faces. For example： Threshold = 5 =

  14. Weight calculation Face verification For example： For all pixels： Potential facial region mask Weight = - 2 - 2 + 6 + 2 + 6 + 6 + 2 + 2 + 2 = 22

  15. Verification Face verification The threshold of frontal view is 4000~5500. The threshold of side view is 2300~2600.

  16. Experimental • Experimental results of gray-level images with simple/complex backgrounds Needs less than 2.5 s to locate thecorrect face position. (200 * 307 pixels) Needs about 28 s to locate the correct face position. (200 * 145 pixels)

  17. Experimental • Experimental results of color images with simple/complex backgrounds

  18. Experimental • Verification of face images with different sizes

  19. Experimental • Verification of face images with altered lighting conditions

  20. Experimental • Verification of face images with distinct positions

  21. Experimental • Verification of face images with changed expressions

  22. Experimental • Verification of defocus face images

  23. Experimental • Verification of face images (a) with noise; (b) with partial occlusion of mouth; (c) wearing sunglasses

  24. Experimental • The face of a cartoon doll; (b) The face of a Chinese doll

  25. Experimental(error detected) • The face is too dark to be detected

  26. Experimental(error detected) • The face with right eye being occluded by the black hair

  27. Conclusions • Detect multiple faces in complex backgrounds • Handle different sizes, different lighting conditions, varying pose and expression, and noise and defocus problems • Partial shelter and side view • The minimum size of a face that could be detected is 5 * 5 pixels • The success rate is approximately 98%.

  28. Comments • 本篇論文對於複雜背景的環境下，需要花費非常久的時間才能正確偵測出人臉的位置，效率不好。 • 在光線不足或是頭髮蓋住眼睛的情況下，會造成無法辨識的情況發生。 • 同時本論文無法能分辨出卡通人物、布偶或是真人 。

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