Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed an algorithm of adaptive dictionary learning according to the inputting testing image. First, nearest neighbors of the testing image are labeled in local configuration pattern (LCP) subspace employing statistical similarity and configuration similarity defined in this paper. Then the face images labeled as nearest neighbors are used as atoms to build the adaptive representation dictionary, which means all atoms of this dictionary are nearest neighbors and they are more similar to the testing image in structure. Finally, the testing image is collaboratively represented and classified class by class with this proposed adaptive over-completed compact dictionary. Nearest neighbors are labeled by local binary pattern and microscopic feature in the very low dimension LCP subspace, so the labeling is very fast. The number of nearest neighbors is changeable for the different testing samples and is much less than that of all training samples generally, which significantly reduces the computational cost. In addition, atoms of this proposed dictionary are these high dimension face image vectors but not lower dimension LCP feature vectors, which ensures not only that the information included in face image is not lost but also that the atoms are more similar to the testing image in structure, which greatly increases the recognition accuracy. We also use the Fisher ratio to assess the robustness of this proposed dictionary. The extensive experiments on representative face databases with variations of lighting, expression, pose, and occlusion demonstrate that the proposed approach is superior both in recognition time and in accuracy.

基于分类的稀疏表示和基于正则化最小二乘的基于协作表示的分类已成功用于人脸识别。对于基于稀疏表示或协作表示的方法，完成过度的字典至关重要，因为它直接确定识别精度和识别时间。本文根据输入的测试图像，提出了一种自适应词典学习算法。首先，使用本文定义的统计相似性和配置相似性，在本地配置模式（LCP）子空间中标记测试图像的最近邻居。然后将标记为最近邻居的人脸图像用作原子来构建自适应表示字典，这意味着该词典的所有原子都是最近的邻居，并且它们在结构上与测试图像更加相似。最后，使用该拟议的自适应超完备紧凑词典，以协作的方式对测试图像进​​行表示和分类。最近的邻居在非常低维的LCP子空间中通过局部二进制模式和微观特征进行标记，因此标记非常快。对于不同的测试样本，最近邻居的数量是可变的，并且通常比所有训练样本的数量少得多，这大大降低了计算成本。此外，此拟议词典的原子是这些高维人脸图像矢量，而不是低维LCP特征矢量，这不仅确保了面部图像中所包含的信息不会丢失，而且原子的结构与测试图像更加相似，从而大大提高了识别精度。我们还使用Fisher比率来评估此拟议词典的鲁棒性。在具有照明，表情，姿势和遮挡的变化的代表性人脸数据库上进行的大量实验表明，该方法在识别时间和准确性上都非常出色。