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This paper explores the use of Two-Dimensional Robust Neighborhood Discriminant Embedding (2D-RNDE) as a means to improve the performance and robustness of face recognition. 2D-RNDE is based on graph embedding framework and Fisher's criterion, it can utilize the original two-dimensional image data directly and takes into account the Individual Discriminative Factor (IDF) which is proposed to describe...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used...
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