Abstract
Face recognition is a challenging problem in computer vision and pattern recognition. Recently, many local geometrical structure‐based techiniques are presented to obtain the low‐dimensional representation of face images with enhanced discriminatory power. However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high‐dimensional data. To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within‐class geometry in two steps, respectively. In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information. Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.
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