Facial Features for Template Matching Based Face Recognition

Problem statement: Template matching had been a conventional method for object detection especially facial features detection at t he early stage of face recognition research. The appearance of moustache and beard had affected the performance of features detection and face recognition system since ages ago. Approach: The proposed algorithm aimed to reduce the effect of beard and moustache for facial features detection a nd introduce facial features based template matching as the classification method. An automated algorithm for face recognition system based on detected facial features, iris and mouth had been d eveloped. First, the face region was located using skin color information. Next, the algorithm compute d the costs for each pair of iris candidates from intensity valleys as references for iris selection. As for mouth detection, color space method was use d to allocate lips region, image processing methods t o eliminate unwanted noises and corner detection technique to refine the exact location of mouth. Fi nally, template matching was used to classify faces based on the extracted features. Results: The proposed method had shown a better features detection rate (iris = 93.06%, mouth = 95.83%) than conventio nal method. Template matching had achieved a recognition rate of 86.11% with acceptable processi ng time (0.36 sec). Conclusion: The results indicate that the elimination of moustache and bear d has not affected the performance of facial featur es detection. The proposed features based template mat ching has significantly improved the processing time of this method in face recognition research.

[1]  Ioannis Pitas,et al.  Face localization and facial feature extraction based on shape and color information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[3]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yu-Shan Li,et al.  The face localization and regional features extraction , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[5]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  N.D. Georganas,et al.  Combining integral projection and Gabor transformation for automatic facial feature detection and extraction , 2008, 2008 IEEE International Workshop on Haptic Audio visual Environments and Games.

[7]  Kin-Man Lam,et al.  An efficient algorithm for human face detection and facial feature extraction under different conditions , 2001 .

[8]  Mohamed Rizon,et al.  Detection of eyes from human faces by Hough transform and separability filter , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[9]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Mohamed Rizon,et al.  Iris detection using intensity and edge information , 2003, Pattern Recognit..

[11]  Jianming Lu,et al.  A method of face recognition based on fuzzy clustering and parallel neural networks , 2004, Signal Process..

[12]  Sungshin Kim,et al.  Real-time face detection and recognition using hybrid-information extracted from face space and facial features , 2005, Image Vis. Comput..

[13]  Se-Young Oh,et al.  Automatic Extraction of Eye and Mouth Fields from a Face Image Using Eigenfeatures and Ensemble Networks , 2004, Applied Intelligence.

[14]  Volkan Atalay,et al.  Projection based method for segmentation of human face and its evaluation , 2002, Pattern Recognit. Lett..

[15]  Vennila Ramalingam,et al.  Real time face and mouth recognition using radial basis function neural networks , 2009, Expert Syst. Appl..

[16]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Yu Song,et al.  Multi-resolution feature extraction in human face , 2004, International Conference on Information Acquisition, 2004. Proceedings..

[18]  Li-Minn Ang,et al.  Automatic model based face feature detection system , 2008, 2008 International Symposium on Information Technology.

[19]  Kuang-chih Lee,et al.  A bottom-up framework for robust facial feature detection , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[20]  Rainer Stiefelhagen,et al.  Multi-stream gaussian mixture model based facial feature localization , 2008, 2008 IEEE 16th Signal Processing, Communication and Applications Conference.