Local binary pattern orientation based face recognition

Scale-invariant feature transform (SIFT) is a feature point based method using the orientation descriptor for pattern recognition. It is robust under the variation of scale and rotation changes, but the computation cost increases with its feature points. Local binary pattern (LBP) is a pixel based texture extraction method that achieves high face recognition rate with low computation time. We propose a new descriptor that combines the LBP texture and SIFT orientation information to improve the recognition rate using limited number of interest points. By adding the LBP texture information, we could reduce the SIFT orientation number in the descriptor by half. Therefore, we could reduce the computation time while keeping the recognition rate. In addition, we propose a matching method to reserve the effective matching pairs and calculate the similarity between two images. By combining these two methods, we can extract different face details effectively and further reduce computational cost. We also propose an approach using the region of interest (ROI) to remove the useless interest points for saving our computation time and maintaining the recognition rate. Experimental results demonstrate that our proposed LBP orientation descriptor can reduce around 30% computation time compared with the original SIFT descriptor while maintaining the recognition rate in FERET database. Adding the ROI at our proposed LBP orientation descriptor can reduce around 58% computation time compared with the original SIFT descriptor in FERET database. For extended YaleB database, our method has 1.2% higher recognition rate than original SIFT method and reduces 28.6% computational time. The experimental results with adding ROI reduces 61.9% computation time for YaleB database.