中文摘要：

针对人脸光照、遮挡、身份、表情等因素变化的人脸姿态估计难题，结合稀疏表示分类（SRC）方法的优秀识别性能，对SRC理论进行了深入分析，并将其应用于人脸姿态分类。为了解决姿态估计中人脸光照、噪声和遮挡变化问题，将人脸姿态离散化为不同的子空间，每个子空间对应一个类别，据此，提出基于字典学习与稀疏约束的人脸姿态识别方法。通过在公开的XJTU和PIE人脸库上实验表明：所研究的方法对人脸光照、噪声和遮挡变化具有鲁棒性。

英文摘要：

According to the challenges in face pose estimation under different illuminations, occlusions, identity, expressions, and so on, combining with the excellent classification performance of sparse representation classification （ SRC ）, a deep analysis on the theory of SRC and its application in face pose classification are made. In order to handle challenges such as variation of face illumination, noises and occlusion, a robust face pose estimation method based on dictionary learning and sparse representation is presented. In which face poses are discrete into different subspaces, each subspace corresponding to a class. Several experiments are performed on XJTU and PIE databases. Recognition results show that the proposed method is suitable for efficient face pose recognition under illumination, noises and occlusion variations.