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We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear...
We propose a new method to retrieve similar face images from large face databases. The proposed method extracts a set of Haar-like features, and integrates these features with supervised manifold learning. Haar-like features are intensity-based features. The values of various Haar-like features comprise our rectangle feature vector (RFV) to describe faces. Compared with several popular unsupervised...
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