人脸识别中的零范数稀疏编码

Zero-Norm Sparse Coding in Face Recognition

为解决人脸识别中运算速度和识别效果之间的矛盾,提出了零范数稀疏编码算法.该算法用零范数捕述稀疏编码模型的稀疏度,通过对模型的间断点连续开拓,有效地提高了算法收敛速度.运用ORL人脸数据库对该算法进行识别率和效率测试,并与非负稀疏编码算法和非负矩阵稀疏分解算法进行对比,表明文中提出的算法调节稀疏度的能力更强,可有效缩短运算时间,并在较短时间内获得较高的识别率.

To avoid conflict between algorithmic efficiency and recognition effectiveness in face recognition, this paper proposes a zero-norm sparse coding algorithm. The algorithm uses zero-norm to describe sparsity of a sparse coding model and applies a strategy of continuous extension of discontinuity points to speed convergence. A test based on the ORL database show that the algorithm is more efficient in adjusting sparsity so that the computation time is reduced, and gives higher recognition rate as compared with the methods of nonnegative sparse coding and non-negative matrix factorization with sparseness constraints.