稀疏综合字典学习的小样本人脸识别

传统以字典学习为基础的小样本人脸识别方法存在字典低辨别性、弱鲁棒性等缺点，对此，本文提出稀疏综合字典学习模型。该模型有效利用和生成人脸变化，以镜像原理及Fisher准则扩充训练样本多样性，通过构造混合特色字典、扩充干扰字典以及低秩字典原子，提取不同类别数据之间的共性、特殊性和异常情况，从而提高算法识别率以及对表情变化、姿态变化、遮挡等异常情况的处理能力。在AR、YALEB、LFW等人脸数据库进行仿真实验，实验结果验证了算法的有效性和可行性。

Sparse comprehensive dictionary learning for small-sample face recognition

Traditional small-sample face recognition methods based on dictionary learning have disadvantages such as poor dictionary discrimination and lack of robustness. In this paper, we propose a sparse comprehensive dictionary learning model. This model effectively utilizes and generates facial changes, expands the diversity of training samples by the mirror principle and Fisher’s criterion, and extracts the commonalities, specialties, and anomalies between different categories of data by constructing a hybrid feature dictionary, extended interference dictionary, and low-rank dictionary atoms. This strategy improves the recognition rate of the algorithm and its ability to handle abnormal situations such as expression changes, pose changes, and occlusions. The results of simulation experiments performed on the face databases AR, YALEB, and LFW verify the effectiveness and feasibility of the proposed algorithm.