| Summary: | 碩士 === 淡江大學 === 資訊工程學系碩士班 === 94 === The machine learning is the state-of-the-art algorithm to solve all kinds of problems. This paper utilizes two types of machine learning algorithm to detect skin and face respectively. First, in the skin detection, to overcome the variance of light on the face is our most essential issue. According to the issue, two features chosen to serve as input of neural network dividedly, the first feature based on YCbCr to conquer the diversity of light, the second feature based on RGB to get over the color near the skin color and we get a binary map. Utilizing Opening and Closing to eliminate the noises and using the proportion of height and width to filter the candidate blocks. Second, in the face detection, the haar-like features[11][12] are utilized to serve as features of modified Adaboost to justify the left, frontal, right, or non-face in the 20 x 20 sliding window.
Experimental results show that the proposed methods reach to better performance. In terms of skin color detection, capacity of coping with the problems of scaling, rotation and multiple faces, it results in good detection rate.
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