关键词: 特征融合, 深度核, 快速收缩迭代, 稀疏表达

Abstract: Considering the information of face feature vector is incomplete and the whole image content has great data amount, this paper presents a method of face recognition based on feature fusion of quasi-matrix neural kernel. This method can be interpreted as the first level lifting operation of deep neural network. Firstly, face data is used as a set of feature vectors, and random matrix sampling is used to form a random feature matrix. Secondly, the stochastic matrix can be mapped to a new feature vectors in a high dimensional space based on deep neural core designed in this paper. The new feature not only overcomes the problem of huge space complexity, but also increases the nonlinear structural characteristics and improves the ability of expression. Finally, a fast iterative shrinkage-thresholding algorithm is proposed to find solution, with the convergence speed is quadratic convergence. Experimental results show that the proposed method has high recognition rate, strong stability and good robustness, is well-suited for processing large data.

Key words: feature fusion, deep neural core, fast iterative shrinkage-thresholding, sparse representation