基于压缩金字塔核稀疏表示的人脸识别

人脸识别中光照、伪装及姿态等变化一直是富有挑战性的问题，其中特征提取是很关键的一步。为提高人脸识别率，结合压缩感知和空间金字塔模型，本文提出了一种新的特征提取方法，首先用尺度不变特征变换算法提取图像特征，然后与随机生成的字典进行稀疏编码，再用金字塔模型分层提取不同尺度空间的特征，并用最大池融合特征，最后运用核稀疏表示分类。在Extended Yale B，AR 和CMU PIE人脸数据库上的实验结果表明，该方法对于人脸图像的光照、伪装及姿态等变化有较强的鲁棒性，而且该算法有较快的运行速度。

Face Recognition Based on Compressed Spatial Pyramid Model and Kernel Sparse Representation

Face recognition is still challenging due to the large variations of facial appearance, caused by lighting, partial occlusions, head pose, etc. The feature extraction is a key step for face recognition. In order to improve the recognition rate of face recognition,we introduce a novel feature extraction technique for face recognition, which is a combination of compressed sensing and spatial pyramid model method. The scale invariant feature transform is first used to be a feature extractor to obtain facial features.Then by using sparse coding in the randomly generated dictionary, dimensionalities of those features are reduced. After the spatial pyramid is used to be a feature extractor to obtain different spatial scales, the max pool is used to integrate the features. Finally, the kernel sparse representation classifier is proposed to classify the features to complete the face recognition. The experimental results based on the Extended Yale B, AR and CMU PIE databases demonstrate that the method has a strong rustness in the illumination, pose and disguise variation with a faster running speed.