Improving ResNet-based Feature Extractor for Face Recognition via Re-ranking and Approximate Nearest Neighbor

This paper proposes a framework for face recognition based on feature extractor from ResNet, together with other steps for performance improvement, including face detection, face alignment, face verification/identification, and re-ranking via Approximate Nearest Neighbor Search (ANNS). First, we evaluate two face detection algorithms, MTCNN, and FaceBoxes on three common face detection benchmarks, and then summarize the best usage scenario for each approach. Second, with certain preprocessing and postprocessing, our system selects the ResNet-based feature extractor, which achieves 99.33% verification accuracy on the LFW benchmark. Third, we use the penalty curve to determine the best configuration and obtain improved results of face verification. Based on the proposed preprocessing and post-processing, our method not only boosts accuracy from 84.3% to 86.5% in large inter-class variation datasets (CASIA - WebFace) but improves Rank-l accuracy from 86.6% to 87.7% in large intra-class variation datasets (FG-NET).