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The 11th International Conference on Computational Science and Its Applications (ICCSA 2011), June 20-23, 2011, Santander, Spain, Accepted. PCA Based Geometric Modeling for Automatic Face Detection Authors : Padma Polash Paul Presented by Padma Polash Paul. Outline. Introduction Face Detection

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  1. PCA Based Geometric Modeling The 11th International Conference on Computational Science and Its Applications (ICCSA 2011), June 20-23, 2011, Santander, Spain, Accepted PCA Based Geometric Modeling for Automatic Face DetectionAuthors :Padma Polash PaulPresentedbyPadma Polash Paul

  2. Outline • Introduction • Face Detection • Skin Color Model • Challenges in Face Detection • Background Study • Proposed Method • Experimental Results • Conclusion and Future Work PCA Based Geometric Modeling

  3. Face Detection vs. Skin Color Model PCA Based Geometric Modeling

  4. Challenges for Face Detection • Face Orientation • Multiple view Face • Background • Time in Massive Processing PCA Based Geometric Modeling

  5. Color Model • Color models for Images • RGB, HSV, YCbCr and CIE-Lab etc • RGB • triple component (RED,GREEN,BLUE) • RGB represents not only color but also luminance. • Luminance: may vary across a person's face PCA Based Geometric Modeling

  6. Sink Color Model • The common RGB representation of color images is not suitable for characterizing skin-color. • Chromatic colors , also known as "pure" colors in the absence of luminance • Normalized color for each pixel R, G and B can be define as chromatic color PCA Based Geometric Modeling

  7. Sink Color Model (Cont’d) • Normalized Color • Normalized R ( ) • Normalized G ( ) • Normalized ( ) PCA Based Geometric Modeling

  8. Sink Color Model (Cont’d) • If two points P1 [r1, g1, b1] and P2 [r2, g2, b2], are proportional, then • Then, P1 and P2 have the same color but different brightness. • Chromatic colors are well suited to segment skin regions from non-skin regions. PCA Based Geometric Modeling

  9. Sink Color Model (Cont’d) • Skin color distribution can be represented by Gaussian Model N (m, c) • From Gaussian fitted skin color model, we can find the likelihood of skin for any pixel of an image. • Establish the threshold for Skin and non skin regions PCA Based Geometric Modeling

  10. Input image or Video (RGB) Segmented RGB skin Regions Converting into Chromatic Color Space Multiply main RGB Image by Black and white template Thresholding image using Skin Color Threshold Apply Region Growing Algorithm Generate Black and White template for skin regions Skin Region Segmentation • Algorithm for Skin Region Segmentation using SCM r = 0.38-0.52 g = 0.23- 0.34 PCA Based Geometric Modeling

  11. Face Detection PCA Based Geometric Modeling

  12. Face Detector Proposed Geometric Face Detector PCA Based Geometric Modeling

  13. Existing Geometric Model • Triangle shape  • Problem • Detect non human face as face PCA Based Geometric Modeling

  14. Geometry of Face • Geometric Shape of face • Masking • More complex interior structure are estimated PCA Based Geometric Modeling

  15. Geometric Modeling of Face • Block diagram of the proposed system Detected Skin Regions Converting into Common Resolution Masking Projecting of PCs Calculate PCA Reconstructing Using Smaller Numbers of PCs Detect Edge Using Canny Edge Detector Normalized the cumulative sum in the rage [0 1] to get the threshold values for face and non face Estimate Threshold Values For Face and Non Face PCA Based Geometric Modeling

  16. Experimental Result • Database Used • California Institute of Technology (CIT) • Baoface dataset (BaoFace) • vision group of Essex University Face Database (Essex), • Georgia Tech Face Database (Georgia Tech) PCA Based Geometric Modeling

  17. Experimental Result PCA Based Geometric Modeling

  18. System Performance Result • For video or larger database • Processing time for face detection is important • Skin region detection is fast because of the thresholding. • Time complexity of the Face detection system is O(1). • After edge detection cumulative sum is compared • Threshold value is rotation invariant because we are taking the cumulative sum of the projected geometric structure PCA Based Geometric Modeling

  19. Conclusion and Future Work • We presented a new method based on modeling of geometric structure of the face method for automatic face detection. • Fusion of PCA based geometric modeling and SCM method provides higher face detection accuracy and improves time complexity. • In the future, using more complex geometric structure can be used for better understanding of the important facial features and threshold values. • Complex structure will also help to obtain a better and more generalized threshold for the face. PCA Based Geometric Modeling

  20. Any Questions? Thanks PCA Based Geometric Modeling

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