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A Proposed Framework for Robust Face Identification System


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Presentation of IEEE paper "A Proposed Framework for Robust Face Identification System"

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A Proposed Framework for Robust Face Identification System

  1. 1. A Proposed Framework for Robust Face Identification System Ahmed F. Gad Faculty of Computers and Information-Menofia University
  2. 2. Outlines System Overview System Phases Image Preprocessing Face Skin Detection (FSD) Facial Features Extraction and Enhancement Algorithms Involved the Proposed System Databases Used and Results References and Conclusion
  3. 3. High Level Description A robust face identification framework to find whether two face images contains represent the same person. Decision Proposed System
  4. 4. Alogirthms Involved Retinex Color Enhancement Frangi Filter Viola~Jones Feature Detector
  5. 5. Proposed System Phases System has 4 major phases: Preprocessin g Skin Detection Feature Extraction Distance Measurement
  6. 6. Retinex Algorithm A color constancy algorithm that use the Fast Fourier Transform and based on RGB. Pixel values are modified to give an estimation of original color and ignoring effects.
  7. 7. Image Preprocessing-Retinex Algorithm Images processed to eliminate degradations and to be invariant to illumination conditions. Results are suitable to Human Visual System (HVS). Retinex
  8. 8. Skin Detection To reduce search space for facial features, skin detection is applied. Rather than searching whole image for features, only selected regions are tested.
  9. 9. Which Color Model To Use ! A survey to compare robustness of different color spaces to detect skin made. HSV 75% YCbCr 83% RGB 92% RGB-H-CbCr 97%
  10. 10. Skin Detection-RGB-H-CbCr Model A robust color model required to detect skin color under large scale with high accuracy. Combination from three color models RGB, HSV, and YCbCr is used. RGB- H-CbCr 97%
  11. 11. Viola~Jones Algorithm A robust object detector based on cascade of simple features. Integral image is a an image representation to facilitate feature detection. Examples of features if the Haar-Like features.
  12. 12. Viola~Jones Algorithm But there are more than 18,000 feature. Just expressive features are selected to minimize computation time using the Adaptive Boosting (AdaBoost) algorithm. More than one classifier are used to detect objects. Algorithm can process 15 frames per second.
  13. 13. Feature Extraction-Viola~Jones Algorithm Object segmentation is applied on the skin binary image. Each extracted object is applied to the algorithm to extract facial features. Face regions only will continue.
  14. 14. Accurate Eye Detection A single eye has three regions: Dark pupil in the eye center. Lighter sclera region surrounding the pupil. Skin surrounding both pupil and sclera. HSV is a robust color space based on its saturation channel that can differentiate among the top two regions.
  15. 15. Eye Pupils Detection Eye centers can be found by analyzing the eye regions for the darkest area using luminance- chrominance model such as HSV.
  16. 16. Frangi Filter Frangi filter is an accurate visualization and quantification of the human vasculature that tries to use human vessel geometrical structures such as Hessian matrix to segment vessel regions. Eigenvalues are extracted from the Hessian matrix to find such features.
  17. 17. Mouth and Nose Enhancement-Frangi Filter Native Viola~Jones algorithm can enlarges the mouth and nose region boundary. Frangi filter can efficiently detect their boundaries.
  18. 18. Distance Measurement The feature vector with 11 metrics gathered from 12 distances is used to compare the two images.
  19. 19. Results The Center for Vital Longevity Face Database is used and system is tested against 2o persons each having 40 different images and yield an accuracy of 98.14%. Computation time is 1.56 seconds.
  20. 20. References  Morel, Jean-Michel, Ana B. Petro, and Catalina Sbert. "Fast implementation of color constancy algorithms." IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2009. pp. 724106-724106  Oliveira, V. A., and A. Conci. "Skin Detection using HSV color space" H. Pedrini, & J. Marques de Carvalho, Workshops of Sibgrapi. 2009.pp:1-2  Kaur, Amanpreet, and Bv Kranthi. " Comparison between YCbCr Color Space and CIELab Color Space for Skin Color Segmentation." International Journal of Applied Info. 3.4 (2012): 30-33.  Ma, Zhanyu, and Arne Leijon. "Human skin color detection in RGB space with Bayesian estimation of beta mixture models." 18th European Signal Processing Conference (EUSIPCO-2010). 2010.  Phung, Son Lam, Abdesselam Bouzerdoum, and Douglas Chai. "A novel skin color model in ycbcr color space and its application to human face detection." Proceedings of International Conference on Image Processing. 2002. Vol. 1. IEEE, pp. I-289.  Bin Abdul Rahman, Nusirwan Anwar, Kit Chong Wei, and John See. "RGB-H-CbCr Skin Colour Model for Human Face Detection." Faculty of Information Technology, Multimedia University (2007).  Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001. Vol. 1. IEEE, 2001.
  21. 21. References  Bob Zhang, Lin Zhang, Lei Zhang, Fakhri Karray “Retinal vessel extraction by matched filter with first-order derivative of Gaussian”, Computers in biology and medicine 40.4 (2010): 438-445.  Minear, M. & Park, D.C. “A lifespan database of adult facial stimuli.” Behavior Research Methods, Instruments, & Computers. 36, .(2004), 630-633.  Dewi Agushinta R, Adang Suhendra, Sarifuddin Madenda, Suryadi H.S. "Face Component Extraction Using Segmentation Method On Face Recognition System." Journal of Emerging Trends in Computing and Information Sciences 2.2 (2011). 67-72  Kim, Hyun-Chul, Sung Yang Bang , Sang-Youn Lee. "Face recognition using the second-order mixture-of-eigenfaces method." Pattern Recognition 37.2 (2004): 337- 349.  Ma, Bingpeng, Yu Su, and Frédéric Jurie. "Covariance descriptor based on bio-inspired features for person re-identification and face verification." Image and Vision Computing 32.6 (2014): 379-390.