An Improved Face Recognition based on Scale Invariant Feature Transform (SIFT): Training for Integrating Multiple Images and Matching by Key Point’s Descriptor-Geometry

Objectives: We proposed for reduction of the computational complexity and improvement of the recognition precision in the face recognition system using Scale Invariant Feature Transform (SIFT) local feature approaches. Methods/Statistical Analysis: The first one is a novel training procedure for the integration of multiple training images. This training procedure performs to remove redundant local features and blend different local features from face image. The second one is a proposed matching scheme not only considering the similarity of key point’s descriptor but also the geometry property through one-to-one matching between query and references images. Findings: This research finds the optimal settings of parameter for the proposed face recognition system based on SIFT. First, we have analyzed the change of recognition rate according to the resolution of face image in the proposed system. Then, to effect of the reduced number of key points per subject on the recognition rate and the resolution of face image were analyzed with the multiple templates per subject. As a result, we observed that the proposed template training procedure using Lowe’s key points detection method with 50×61 resolution of face images achieves higher recognition rate than the holistic approaches. The usage of Geng’s key point detection method in the proposed system obtains higher recognition rate than the usage of Lowe’s method. From the experimental result with ORL databases, the proposed face recognition system gives 99.5% of rate, which shows the higher performance than the previous ones. In addition, the proposed integration method of multiple training images reduces the number of key points by average 49.84% than the method of using multiple templates. Improvements/Applications: The experimental result of the proposed system in two well-known face databases shows that the computational quantities are reduced effectively compared to other SIFT based methods, and it gives better performance on face recognition accuracy.