Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
This work addresses face recognition challenges in complex visual environments for video surveillance or security applications, but it is incremental as it builds on existing methods like RetinaFace.
The paper tackles face recognition in complex video environments by proposing an image pre-processing module, a faster detection method (RetinaFace-mnet-faster), and a secondary search mechanism, resulting in accuracy improvements of up to 1.4% on datasets and speed gains of up to 82% compared to baseline methods.
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.