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To improve the efficiency of a face detector, this paper presents an automatic distributing detector (ADD) based on the fuzzy theory to improve the performance of face detection. The main contributions lie in:l) A new Haar-like feature representation based on the fuzzy membership function is proposed, 2)The entropy of feature set is employed as choice criteria to select weak classifiers, 3) The AdaBoost...
In order to improve efficiency of face detector, fuzzy set theory is used in establishing distribution face detector. This detector trains the sample set by Haar-like feature and membership function, and selects appropriate weak classifiers through the feature setpsilas entropy and AdaBoost learning algorithm. Subsequently distribution face detector is established and tested on the MIT+CMU frontal...
Face detection plays an important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based object detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we have adopted greedy sparse...
An efficient algorithm for facial features extractions is proposed. The facial features we extracted are the edges of two eyes, nose and mouth. The algorithm is based on an improved Gabor wavelets edge detector to detect the face region and facial features regions. The experimental results show that the proposed method is robust against facial expression, illumination, and can be also effect if the...
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