关键词: 二维偏最小二乘, 非负性, 人脸识别, 二维非负偏最小二乘

Abstract: Traditional subspace statistic methods, such as Principal Component Analysis（PCA） can only get a series of eigen face through learning, the available local features（eyes, nose） for face recognition are ignored. However, these methods incorporating the category information such as Linear Discriminant Analysis（LDA）, face small sample problems. In order to take over these disadvantages, the paper proposes a novel approach to extract the facial features called Two-Dimension Nonnegative Partial Least Squares（2DNPLS）. The main idea of the approach is grabbing the local features via adding the constraint of nonnegative to 2DPLS, which makes the approach gain not only the advantages of 2DPLS, incorporating both inherent structure and category information of images, but also the local features, having nonnegative interpretability. For evaluating the approach’s performance, a series of experiments are conducted on two famous face image databases ORL, Yale face databases, which demonstrate that the proposed approach outperforms the state-of-art algorithms.

Key words: Key words： Two Dimension Partial Least Squares（2DPLS）, nonnegative, face recognition, Two Dimension Nonnegative Partial Least Squares（2DNPLS）