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Iris Detection and Segmentation

Iris Detection and Segmentation. Dat Chu, Michael Fang, Paul Hernandez Herrera, Homa Niktab , Danil Safin. Problem Statement.

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Iris Detection and Segmentation

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  1. Iris Detection and Segmentation Dat Chu, Michael Fang, Paul Hernandez Herrera, HomaNiktab, DanilSafin

  2. Problem Statement To detect and segment an iris under single pose, single lighting condition as close to real-time as possible. The video can be of different subjects, different gazes and perhaps with different backgrounds.

  3. Approaches We will use two different approaches to this problem then provide a comparison between them. • Feature-based approach • Topographic approach

  4. The Feature-based Approach • Detect eye location • Segment eye using skin tone • Segment iris with histogram-based method and Hough Transform (of edge images)

  5. Preliminary Results (Feature-based) • Eye detection using Viola-Jones method (part of OpenCV) • Gaussian filter to remove spurious edges • Canny Edge Detection of eye region to remove most edge except those of eye-lids and iris • Hough Transform

  6. Next Steps (Feature-based) • Skin-tone segmentation prior to edge detection and Hough transform using Otsu thresholding. • Stabilize Hough transform results by incorporating temporal information (e.g. Kalman filter) • Expand iris detection method to account for iris shape deformation at different gaze angles.

  7. The Topographic Approach • Smooth the image using Gaussian filter • Consider the image as a 3D surface (gray intensity at (x,y) location as z) • At each (x,y) consider a squared neighborhood and fit a set of Chebyshev polynomials to retrieve the continuous function, from which the Hessian matrix can be obtained • Apply Eigenvalue decomposition and obtain the Eigenvalues, which are used to provide a terrain type for (x,y) • List all "pit" locations as eye candidates • For each pair of candidates, generate a rectangular patch around each candidate and use the terrain type labels as feature vector for SVM classification with Bhattacharyya kernel  • Based on the initial detection, search for the eye location in the subsequent frames using Mutual Information

  8. Advantages • No face tracker required • Detection and tracking on the topographic manifold – easier PDF estimation and faster MI calculation (12 types of topographic features vs. 256 gray levels) • Optimal match can almost always be found in “pit” pixels – more constrained search

  9. Expected Results (Topographic) <<<TBA>>>

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