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- W3211749523 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." @default.
- W3211749523 created "2021-11-22" @default.
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- W3211749523 date "2021-01-01" @default.
- W3211749523 modified "2023-09-26" @default.
- W3211749523 title "Video Face Recognition System: RetinaFace-Mnet-Faster and Secondary Search" @default.
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- W3211749523 doi "https://doi.org/10.1007/978-3-030-73103-8_44" @default.
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