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This paper presents a novel method for solving face gender recognition problem. This method employs 2D Principal Component Analysis, one of the prominent methods for extracting feature vectors, and Support Vector Machine, the most powerful discriminative method for classification. Experiments for the proposed approach have been conducted on FERET data set(More)
The paper presents a novel approach for solving face recognition problem. We combine Gabor filters and Principal Component Analysis (PCA) to extract feature vectors; then we apply Support Vector Machine (SVM), the most powerful discriminative method, and AdaBoost, a meta-algorithm, for classification. Experiments for the proposed method have been conducted(More)
This paper presents a new method for solving face gender identification and face classification problems. The proposed method uses gradient features for feature extraction and support vector machine for classification. Experiments for the proposed method have been conducted on two public data sets CalTech and AT&T. The results show that the proposed(More)
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