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Face Detection with color eigenfaces

Face Detection with color eigenfaces. Underlying Theory Finding the Color Eigenfaces Algorithm Results Conclusion. Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project. Theory [D. Tzovaras, D. Koustos and M.G. Strintzis]. MxN input image. Face class.

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Face Detection with color eigenfaces

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  1. Face Detection with color eigenfaces • Underlying Theory • Finding the Color Eigenfaces • Algorithm • Results • Conclusion Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project

  2. Theory[D. Tzovaras, D. Koustos and M.G. Strintzis] MxN input image Face class Mahalanobis distance: Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project

  3. Eigenfaces[Sirovich and Kirby] Eigen Face #1 Eigen Face #2 Eigen Face #3 Eigen Face #4 10 10 10 10 20 20 20 20 30 30 30 30 40 40 40 40 50 50 50 50 20 40 20 40 20 40 20 40 5 5 5 5 10 10 10 10 15 15 15 15 20 20 20 20 25 25 25 25 10 20 10 20 10 20 10 20 5 5 5 5 10 10 10 10 15 15 15 15 20 20 20 20 25 25 25 25 10 20 10 20 10 20 10 20 • 30 training images (remove their mean) • Average face + 4 first eigenfaces: Y U V Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project

  4. 50 100 150 200 250 50 100 150 200 250 300 350 Probability Probability 50 50 100 100 150 150 200 50 100 150 200 250 300 100 200 300 Probability thresholding Probability thresholding 50 50 100 100 150 150 200 50 100 150 200 250 300 100 200 300 Input Image Convert to YUV and rescale at 1:1 and 1:1.4 Scaling 1:1 Scaling 1:1.4 Compute probability maps Probability maps of different scalings Thresholding Binary maps of different scalings Sum results of the different scalings Sum of binary maps Clustering Face centers

  5. Results Training . 3 50 100 150 200 250 300 350 400 450 100 200 300 400 500 600 Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project

  6. Conclusion • Weaknesses: • Algorithm mainly detects skin • Sensitive to contrast • Advantages: • Quick training (30 training faces required) • Quick processing (3 min for 81% efficiency) • Improvements: • Consider a non-face space in the ML detection for a better accuracy (SVM) Ying Xu, Xavier Brolly, and Jean-Philippe Ronc EE368: Face Detection Project

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