Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search

TL;DR

This paper presents a comprehensive video face recognition system that enhances image quality, employs a novel detection model, and introduces a secondary search mechanism, significantly improving accuracy and speed in complex environments.

Contribution

It introduces RetinaFace-mnet-faster for detection, an image pre-processing module, and a secondary search with HNSW, advancing face recognition in complex video scenes.

Findings

Image pre-processing improves recognition accuracy.

RetinaFace-mnet-faster increases detection speed.

Secondary search with HNSW enhances large-scale dataset performance.

Abstract

Face recognition is widely used in the scene. However, different visual environments require different methods, and face recognition has a difficulty in complex environments. Therefore, this paper mainly experiments complex faces in the video. First, we design an image pre-processing module for fuzzy scene or under-exposed faces to enhance images. Our experimental results demonstrate that effective images pre-processing improves the accuracy of 0.11%, 0.2% and 1.4% on LFW, WIDER FACE and our datasets, respectively. Second, we propose RetinacFace-mnet-faster for detection and a confidence threshold specification for face recognition, reducing the lost rate. Our experimental results show that our RetinaFace-mnet-faster for 640*480 resolution on the Tesla P40 and single-thread improve speed of 16.7% and 70.2%, respectively. Finally, we design secondary search mechanism with HNSW to improve performance. Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.