Biomedical Person Identification via Eye Printing

1. Biomedical Person Identification via Eye Printing Masoud Alipour (malipour@ipm.ir) Ali Farhadi (farhadi@ipm.ir) Nima Razavi (n_razavi@ce.sharif.edu) IPM – Scientific Computing Center Vision Group Institute for Studies in Theoretical Physics and Mathematics Tehran-Iran

2. Outline Introduction to human eye and Iris structure • Human Eye and Iris structure and properties of Human Iris Image De-noising • Application of wavelet analysis. Iris Locating • Creating Edge Image and Circular Hough Transform. • Find Ciliary and Pupillary Boundaries. Feature Extraction • Application of Higher Order Statistics (creating LPC Matrix). • Discrete Cosine Transform (DCT). • Analysis of Geometric Characteristics of obtained Surface. • Frequency Domain Analysis and FFT. Feature Classification

3. Introduction to human eye structure • Eye Structure : Fig1.Human Eye

4. Human Iris Structure Anterior layer of Human Iris : 1. Pigment frill 2. Pupillary area 3. Collarette 4. Ciliary area 5. Crypts 6. Pigment spot

5. Biometric Properties Of Human Iris features • 1. crypts . • 2. pigment spot. • 3. radial and concentric furrows . • 4. collarette. • 5. pigment frill. Collarette Concentric furrows Radial furrows

6. Outline Introduction to human eye and Iris structure • Human Eye and Iris structure and properties of Human Iris Image De-noising • Application of wavelet analysis. Iris Locating • Creating Edge Image and Circular Hough Transform. • Find Ciliary and Pupillary Boundaries. Feature Extraction • Application of Higher Order Statistics. • Discrete Cosine Transform (DCT). • Analysis of Geometric Characteristics of obtained Surface. • Frequency Domain Analysis and FFT. Feature Classification

7. Image De-noising Application of Daubechies wavelet to remove • High frequency noise introduced by camera • Reflection noise

8. Outline Introduction to human eye and Iris structure • Human Eye and Iris structure and properties of Human Iris Image De-noising • Application of wavelet analysis. Iris Locating • Creating Edge Image and Circular Hough Transform. • Find Ciliary and Pupillary Boundaries. Feature Extraction • Application of Higher Order Statistics. • Discrete Cosine Transform (DCT). • Analysis of Geometric Characteristics of obtained Surface. • Frequency Domain Analysis and FFT. Feature Classification • Neural Networks for classification

9. Iris Locating Iris Locating is achieved by : Creating Edge-Image • Circular Hough Transform of Edge Image. • Locating Ciliary Boundary. • Locating Pupillary Boundary . • Creating Iris Image ( Polar indices ).

10. r r2 r1 (x,y) x Circular Hough Transform • 1. Description of circular Hough space • 2. Normalizing the Hough Space • 3. Locating center and radius of the cilirary boundary. y

11. Iris Locating Results : Fig 1. Fig 2. Original Image Edge-Image

12. Iris Locating Maximum point Fig 4. Fig 3. Circular Hough Space (one layer) Iris Image

13. Outline Introduction to human eye and Iris structure • Human Eye and Iris structure and properties of Human Iris Image De-noising • Application of wavelet analysis. Iris Locating • Creating Edge Image and Circular Hough Transform. • Find Ciliary and Pupillary Boundaries. Feature Extraction • Application of Higher Order Statistics. • Discrete Cosine Transform (DCT). • Analysis of Geometric Characteristics of obtained Surface. • Frequency Domain Analysis and FFT. Feature Classification

14. Feature Extraction • -Application of Higher Order Statistics. • -Discrete Cosine Transform (DCT) Analysis. • -Analysis of Geometric Characteristics of Surface • of LPC coefficients. • -Frequency Domain Analysis and FFT. • - Circular DCT

15. Higher Order StatisticsCreating Sectors • Each sector is defined by 4 parameters (rmin ,rmax ,thmin ,thmax ) • We create sectors from rmin to rmax and moving counter-clockwise from thmin to thmax with large overlaps. overlapping Sectors

16. zoom Higher Order StatisticsDefinition of LPC Coefficients Neighborhood Configuration

17. Higher Order StatisticsDefinition of LPC Coefficients • Linear Predictive Coding S = Sector Index N = neighborhood configuration (o N ) X(p) = brightness of pixel p (value of the pixel)

19. DCT Analysis 1. From the average of the nearest four horizontal and vertical neighbors we obtain a matrix A. For ease of references we call this matrix as PLPC. 2. Defining a square w * w window W on the PLPC Matrix. 3. Computing DCT Coef of each window. 4. As window W moves along a row , the curve C is obtained by calculating ||Differences of DCT coefficients of two contiguous windows ||2 • Hence for each row we obtain a curve. Averaging these curves over different rows , we obtain a curve which we call FC. • Curve FC is the first part of our feature vector.

20. Feature Vector DCT of PLPC ?

21. Geometric Characteristics of PLPC Surface • Each sector is identified by • initial ρ and θ . • Each (ρ,θ) together with corresponding entry of PLPC matrix give a surface (PLPC surface).

22. PLPC Surface Zs Zs’

23. Geometric Characteristics of PLPC Surface • 1.Trinagulation of PLPC Surface. • 2. Mapping gravity center of each triangle on plate z=0 • 3. Centroid Matrix • 4. Statistical invariants of Centroid matrix are next elements of the feature vector.

24. Triangulation

25. 4 6 3 2 4 5 1 3 3 3 3 6 Centroid Matrix

26. Statistical invariants of Centroid matrix • We make use of Mean , Variance and Kurtosis of Centroid Matrix. • These three invariants are next 3 elements of the feature vector. Recall that Kurtosis(X) =E[X4] – 3*E[X2]2 .

27. Feature Vector DCT of PLPC Mean of Centroid Matrix Variance of Centroid Matrix Kurtosis of Centroid Matrix ?

28. Frequency Domain Analysis and FFT • Let D be the differences of consecutive columns in matrix of LPC Coef. • These quantities can be regarded as function on set of 20 points. • Calculate FFT of this function. Thus transferring data to Frequency Domain. (resulted in C20) • Make use of absolute values to transfer data to R20. • Projecting the data to 3D subspace. • Application of Geometric Properties of 3d obtained scatter plots

29. Geometric Properties of 3D scatter plots • The next member of The feature vector is the volume of the convex closure of the projected data.

30. Feature Vector DCT of PLPC Mean of Centroid Matrix Variance of Centroid Matrix Kurtosis of Centroid Matrix Volume of the convex closure of fft coef ?

31. Circular DCT • Scanning the Iris Layer by Layer( Each Layer is a circle ) and obtain Vector C. • Calculating DCT coefficients of C . • By merging results of all layers, we obtain a Matrix. • Kurtosis of this matrix is the next element of the Feature Vector.

32. Feature Vector DCT of PLPC Mean of Centroid Matrix Variance of Centroid Matrix Kurtosis of Centroid Matrix Volume of the convex closure of fft coef Kurtosis of circular DCT

33. Analysis of geometric characteristics of CDCT • Applying Circular DCT , we obtain a high dimensional data set. • Make use of projection to reduce dimensionality of the data to 1D data (by average) 3. Triangulation of PLPC Surface. • Mapping mass center of each triangle on plate z=0 • Centroid Matrix • Kurtosis of centroid matrix is the last element of the feature vector.

34. Feature Vector DCT of PLPC Mean of Centroid Matrix Variance of Centroid Matrix Kurtosis of Centroid Matrix Volume of the convex closure of fft coef Kurtosis of circular DCT Kurtosis of Centroid Matrix of circular DCT

35. Feature Classification • Feature vector has been tested on a small data base of about 35 irises. • So far has produced no type 1 or type 2 errors. • Remains to be tested on a large data base.

36. Questions?