论文标题
视频面部识别系统：视网膜范围和次要搜索
Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
论文作者
论文摘要
面部识别在场景中广泛使用。但是，不同的视觉环境需要不同的方法，并且面部识别在复杂的环境中很难。因此，本文主要是视频中实验复杂的面孔。首先，我们为模糊场景或暴露面孔设计一个预处理模块以增强图像。我们的实验结果表明，预处理的有效图像分别提高了0.11％，0.2％和1.4％的LFW，较宽的面部和我们的数据集的精度。其次，我们提出了视网膜范围的检测和面部识别的置信阈值规范，从而降低了损失率。我们的实验结果表明，在特斯拉P40上的640*480分辨率的视网膜面 - 速度分别提高了16.7％和70.2％。最后，我们使用HNSW设计辅助搜索机制以提高性能。我们的适合大规模数据集，实验结果表明，我们的方法比单帧检测的猛烈检索快82％。
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.
