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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)
In this paper, we propose a new approach to Bayesian subspace method for face recognition. We review this method and point out some weak points in its assumptions and propose a practical solution to overcome those weak points. In addition, we present an efficient way to estimate the high dimensional Gaussian density function.