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To improve the performance of multi-pose face detection, the AdaboostSVM algorithm based on multi-feature fusion is proposed in this paper. Firstly, the Haar-like features and the triangular integral features are introduced and the edge-orientation field features based on morphological gradient are presented. Then, the AdaboostSVM Algorithm based on the above three kinds of features is proposed. The...
In order to improve the training convergence speed and detection accuracy of diverse AdaBoostSVM, an improved algorithm is proposed according to the asymmetry in face detection. In the algorithm, the weight of each weak learner, which represents importance of each weak learner, is determined by the error rate and the recognition capability of the weak learner for the face samples. The results of the...
The proposed null Foley-Sammon transform (NFST) method based on the Gram-Schmidt orthogonalization successfully overcomes the so-called small sample size problem with high performance in terms of recognition accuracy and low computation cost, however, the NFST method is still a linear technique in nature, so a new nonlinear feature extraction method called kernel null Foley-Sammon transform (KNFST)...
Orthogonal discriminant analysis algorithms have recently been proposed. However, these methods donpsilat address the singularity problem in the high dimensional feature space. In this paper, we present a new method called direct orthogonal discriminant analysis (DODA), which is able to extract all the orthogonal discriminant vectors simultaneously in the high-dimensional feature space and does not...
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