Face Recognition Using Overcomplete Independent Component Analysis

Jian Cheng, Hanqing Lu, Yen-Wei Chen, Xiang-Yan Zeng

Abstract: Most current face recognition algorithms find a set of basis functions in a subspace by training the input data. However, in many applications, the training data is limited or only a few training data are available. In the case, these classic algorithms degrade rapidly. The overcomplete independent component analysis (overcomplete ICA) can separate out more source signals than the input data. In this paper, we use the overcomplete ICA for face recognition with the limited training data. The experimental results show that the overcomplete ICA can improve efficiently the recognition rate.