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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/14832

Title: Facial features for template matching based face recognition
Authors: C.T. Yuen, M. Rizon
Keywords: Facial features, face detection, iris detection, mouth detection, face recognition
تاريخ النشر: 2009
Publisher: Science Publications, New York
Abstract: Problem statement: Template matching had been a conventional method for object detection especially facial features detection at the 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 and 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 developed. First, the face region was located using skin color information. Next, the algorithm computed the costs for each pair of iris candidates from intensity valleys as references for iris selection. As for mouth detection, color space method was used to allocate lips region, image processing methods to eliminate unwanted noises and corner detection technique to refine the exact location of mouth. Finally, 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 conventional method. Template matching had achieved a recognition rate of 86.11% with acceptable processing time (0.36 sec). Conclusion: The results indicate that the elimination of moustache and beard has not affected the performance of facial features detection. The proposed features based template matching has significantly improved the processing time of this method in face recognition research.
URI: http://hdl.handle.net/123456789/14832
يظهر في المجموعات:College of Applied Medical Sciences

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