基于局部特征的自适应加权2DPCA的人脸识别

针对二维主成分分析(2DPCA)提取的是人脸的全局特征，但局部特征对人脸识别的作用非常大，提出了一种基于局部特征的自适应加权2DPCA。该算法首先根据局部特征把人脸图像分为上中下三个独立的子块，2DPCA应用到每个子块,自适应地计算出每个子块对识别的不同预期贡献，并把此预期贡献值作为子块权重加权到分类器中以提高识别率，实验结果证明了此算法的有效性和可行性。

Adaptively weighted 2DPCA based on local feature for face recognition

Two Dimensional Principal Component Analysis (2DPCA) extracts the global feature of human face, but the local feature is very important to face recognition. In this paper, adaptively weighted 2DPCA based on local feature was proposed. Firstly, the face image was separated into three independent sub-blocks according to the local features. Secondly, 2DPCA was applied to the sub-blocks independently. Then the method can adaptively compute the contributions made by each sub-block and endow them to the classification in order to improve the recognition performance. The experiments on the ORL and Yale face databases demonstrate the proposed method's effectiveness and feasibility.