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Face Detection Using Skin Color and Gabor Wavelet Representation

Face Detection Using Skin Color and Gabor Wavelet Representation. Resmana Lim, Marcel J.T. Reinders email: resmana@it.et.tudelft.nl. Information and Communication Theory Group Faculty of Information Technology and System Delft University of Technology Delft - Netherlands.

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Face Detection Using Skin Color and Gabor Wavelet Representation

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  1. Face Detection Using Skin Color and Gabor Wavelet Representation Resmana Lim, Marcel J.T. Reinders email: resmana@it.et.tudelft.nl Information and Communication Theory GroupFaculty of Information Technology and SystemDelft University of TechnologyDelft - Netherlands

  2. Agenda of Presentation • Introduction • Extraction of Face Candidate Regions • Face Detection in Face Candidate Regions • Graph Matching by Genetic Algorithm • Experiment Results • Conclusion

  3. Introduction • Face detection and detecting facial landmarks (such as position of eyes, nose, mouth, etc.) play an important role in face recognition systems • This paper focuses on the robust and accurate detection of landmark points on the face • The approach first uses color information to detect face candidate regions and then uses a deformable graph matching to locate facial landmark points in these candidate regions • The method is made robust against lighting variantions and variations between people by representing the landmark points using Gabor filter responses • This paper introduce an alternative matching process by using a Genetic Algorithm (GA) to optimize the matching criteria in finding faces candidate regions and detection of the facial landmarks

  4. Extraction of Face Candidate Regions • First step, to separate skin regions from non-skin regions based on color information Main objective is to reduce search space for the faces drastically The identification of the facial region is determined by utilizing a priori knowledge of the skin distribution in the HS color space

  5. Extraction of Face Candidate Regions • Second step, to smoothen object silhouettes, and also to eliminate any isolated misclassified pixels that may appear as impulsive-type noise • Third step, to fill the holes in the face • Finally, small isolated regions that remain after this step are removed

  6. Extraction of Face Candidate Regions Flow Diagram of Skin Region Extraction

  7. Extraction of Face Candidate Regions

  8. Face Detection in Face Candidate Regions • We need identify for each face candidate region whether it is a face or not and if so what the position of the landmarks points are • For indentifying face candidate region is a face or not, we apply a graph matching procedure based on Genetic Algorithm Matching the face candidate regions againts face model graph • facial landmarks in the face region is found by maximizing the simmilarity between the face model graph and the face region image Each facial landmark is represented by the expected local Gabor filter responses in the image

  9. Face Detection in Face Candidate Regions 2-D Gabor Filter Kernel Where

  10. Face Detection in Face Candidate Regions j=1,..,m Single Gabor Filter Response Gabor Jet This research uses eight different orientations (n=8) and four different wavelengths (=3,5,7,10) resulting in 32 filter response

  11. Face Detection in Face Candidate Regions Face model consisting of four landmark points (p1,…,p4) represented by their Gabor filter bank responses

  12. Graph Matching by Genetic Algorithm Objective function Where Crossover rate = 1, mutation rate = 0.01 80 individuals at each generation

  13. Graph Matching by Genetic Algorithm Solution is represented by five parameters • x and y position (coordinate of the left eye) • scaling factor in x and y direction, rotation angle • rotation angle The threshold value of 0.7 is used to judge whether the candidate face region constitutes a face or not.

  14. Experiment Result Facial Landmark localization

  15. Conclusion • We have proposed a detection scheme for locating facial landmarks based on color information and graph matching using a genetic algorithm as optimization strategy. • The performance of the proposed method was demonstrated on various color images containing single and multiple faces. • The results are quite promising for frontal pose faces with moderate rotation and tilting. From the results of the experiment, we conclude that the proposed method has a good prospect and should be considered in the design of face recognition systems

  16. Thank You

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