Abstract
Illumination variation and facial expression generally causes performance degradation of face recognition systems under real-life environments. In traditionally, Scale-invariant feature transform (SIFT) has good result for scale-variance and rotation, but the recognition is lower in illumination variation, and requires high computation complexity. Therefore, we propose a fast descriptor and matching method on SIFT, using the local binary patterns orientation and histogram equalization to remove the lighting effects. This method has the following advantages: (1) Remove the lighting influence effectively. (2) Extract different face details. (3) Reduce computational cost. We also propose using region of interest to remove the useless interest points for saving our computation time and maintaining the recognition rate. Experimental results demonstrate that our proposed has 0.8\% higher recognition rate than original and reduces 28.3\% computation time for FERET database has 1.2\% higher recognition rate than original and reduces 28.6\% computational time compared to original. In the ROI systems, experimental results demonstrate that our proposed reduces 61.9\% computation time and has 75.7\# recognition rates for FERET database has 95.2\% recognition rate original and reduces 57.4\% computational time.