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Iris detection using intensity and edge information

Iris detection using intensity and edge information. Pattern Recognition 36 (2003) 549-562. Speaker: Jing Ming Chiuan ( 井民全 ) Feb. 21 2005. Outline. Introduction Outline of the proposed algorithm Extraction of the face regions Extraction of valleys in the face region

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Iris detection using intensity and edge information

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  1. Iris detection using intensity and edge information Pattern Recognition 36 (2003) 549-562 Speaker: Jing Ming Chiuan (井民全) Feb. 21 2005

  2. Outline • Introduction • Outline of the proposed algorithm • Extraction of the face regions • Extraction of valleys in the face region • Detection of iris candidates • Selecting the irises of both eyes • Experimental results

  3. Introduction • The face recognition has many applications • Personal identification, • Criminal investigation, • Security work, • Login authentication. • Face recognition takes among facial features, such eyes, and mouth

  4. Introduction • The eyes can be considered salient and relatively stable features • The other feature can be estimated using the eye positions • Eye Detection • Eye position detection • Eye contour detection • Deformable template

  5. Detect only eye patterns that are similar to sample eye image Introduction • Eye position detection • Template matching • Eigenspace • Separability filter Eigenspace training samples

  6. Outline of the proposed algorithm Valley detection Face region detection Candidate location Candidate radius detection Iris selection

  7. That is head rotation about the y-axis is (-30,+30) About the input image • The image is • an intensity image or a colour image • a head-shoulder image with plain background • The irises of both eyes appear in the image example

  8. M N V(x) x Extraction of the face regions • For intensity images Step 1 Input Image Step 2 Sobel Edge operator Step 3 Calculate the left and right bound more

  9. Left bound: The smallest x value which > V(X0)/3 Right bound: The largest x value which > V(X0)/3 Y min  the smallest y that H(y) >=0.05(Xr-Xl) V(x) Xl Xr Y min x Y min+1.2(Xr-Xl) X0= the largest V(x) Y max Face Region Extraction

  10. Extraction of the face regions Reference from J. Yang, A. Waibel, Real-time face tracker, Proceedings of the 3th IEEE Workshop on Applications of Computer Vision, 1996 • For color images • A skin-color model is used • the r-g color space Step 1 collect the skin region on the face images Step 2 Build the Gaussian distribution model

  11. The probability density function The mean vector The covariance matrix The Gaussian Distribution Referece: http://mathworld.wolfram.com/NormalDistribution.html Non-skin skin Basic normal distribution

  12. Extraction of the face regions-- for color image Step 1 Select pixels (x,y) whose color values v=(r,g) satisfy g(v) > ε Skin-color pixels Step 2 Apply a closing and an opening to the region of the skin-color pixels 5x5 9x9 Noise removing Step 3 Find the connected component with the largest area. Step 4 the boundary of the area is defined as the face region

  13. neighborhood maximum operator Ref: http://www.astro.princeton.edu/~esirko/idl_html_help/D24.html#wp747869 If(V(x,y)>T) V(x,y) valley pixel Else V(x,y)  non-valley pixel - Original Grayscale closing Extraction of valleys in the face region Valley detection Face region detection binary image Extract the valley image V(x,y)=G(x,y) - I(x,y), Gray scale closing

  14. is the maximum of V(x,y) =0.1 is the # of pixels in the region such that V(x,y)= i is the # of pixels in the region • The threshold value was set to the largest value T satisfying Collect 10% of valleies h(T) h(T+1) h(T+n) h(MAX)

  15. Reference from C.H. Lin, J.L. Wu, Automatic facial feature extraction by genetic Algorithm, IEEE Trans. Image Process, 1999 Select m pixels according to nonincreasing order of C(x,y) (m=20) Vc= 1 1 1 1 1 Vr(0)=2 Vr(1)=2 Vr(2)=1 Vr(3)=2 Detection of iris candidates Computers the valley costs Candidate location

  16. Perfect match case P2 smaller  B smaller  η smaller P2larger B larger  ηlarger example Reference from K. Fukui, O. Yamaguchi, Facial feature point extraction method based On combination of shape extraction and pattern match, Trans. IEICE Japan 1997 Measure the separability

  17. Perfect match case η1 η2ηtηt+1ηu Find the optimal radius • Changing the size r in the range, we find the size r maximizingη(r) For AR face database rl =10, ru=13 For Bern face database rl=5, ru=7

  18. Selecting the irises of both eyes

  19. Bi亮度越低越好 => 小 越 balance 越好 => 小 The factor of separation balance 越 balance 越好 => 小 The cost of an iris • Let B iare the iris candidates • Compute the cost for B i The Hough transform vote

  20. 2r 2r r r Largest vote (a,b) The Hough Transform Edge point r (Xi,Yi) Edge point

  21. Costs for pairs of iris candidates Bi Bj dij The width of the face region The pair of the eyes The cost of a pair of iris candidates Bi and Bj The cost of an iris Cross-correlation value

  22. The template for AR face database Cross-correlation value Step 1 Matching the template Affine Transform Step 2 the correlation value The eye templates for Bern face database R(i,j)= Step 3 if R(i,j) <0.1 then R(i,j)=0.1

  23. Select the lowest F(i,j) as eye pair

  24. Experimental Results • Two databases are used • The face database of University of Bern • The AR face database

  25. The face database of University of Bern Size: 512 x 342 with 150 faces without spectacle

  26. 95.3% (avg) The proposed algorithm is not sensitive to the variation of the template All 150 faces 120 faces without The looking-down faces

  27. False detection of the irises

  28. The execution time PentiumIII 700Mhz

  29. More valley pixels

  30. all correlation factor No correlation factor

  31. Comparison Excluding training set

  32. The AR face database • Color images with size 768x576 without spectacles • Natural illumination condition with different expressions • 63 face images are used Success rate = 96.8%, false image=2

  33. Histogram Equalization Remove the light spot The light spot Step 2 Replace by the smallest intensity value

  34. Successful images

  35. Conclusion • We proposed a new algorithm to extract the irises of both eyes • A simple method is used to remove the light spot of the eye

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