Face Recognition Based on Shearlet Transform and Fast ICA
Xiaojie Sun, WU Xi-sheng, Yuena Wei · 2014
In this paper, a method of face recognition based on shearlet transform and fast independent component analysis (Fast ICA) is proposed to overcome the disadvantage of shearlet transform, which is easy to have data redundance in extracting features. First of all, the coefficients of different scales and directional sub bands are obtained after using shearlet transform to the face images, then using Fast ICA for further extraction to eliminate the high-level redundancy. At last, using support vector machine for classification. In ORL face databases, the experimental results show that the algorithm has a high recognition performance and can capture the facial features effectively.