Insightface结合Faiss的高并发人脸识别技术研究

在高并发、多实例等业务模拟场景下，测试人脸检测与对齐、特征提取、特征匹配检索过程，并进行人脸识别算法效率和精度的优化。利用MTCNN及改进的Insightface算法、Faiss框架，基于LFW数据集，以Face++提供的API做参照。分析结果表明，特征提取1v1比对精度达99.76%，1vN比对精度达95.23%，特征提取效率每秒事务处理量达7.84，特征匹配效率较传统算法提升2个数量级。该项人脸识别技术的研究为铁路未来实施超大规模人像库的动态安防布控提供技术支撑。

Highly concurrent face recognition technology with Insightface and Faiss

In highconcurrency, multiinstance and other business simulation scenarios,this paper tested the process of face detection, feature extraction, feature matching retrieval, and optimized the efficiency and accuracy of face recognition algorithm. The paper used MTCNN, improved Insightface algorithm and Faiss frame, based on LFW data set, compared the extracted features with the API provided by Face++. The analysis results show that the precision of feature extraction is 99.76% for 1v1 and 95.23% for 1v N. The efficiency of feature extraction is 7.84 per second. The efficiency of feature matching is two orders of magnitude higher than that of traditional algorithms. The research on this face recognition technology provides technical support for railway to carry out the dynamic security control of super scale person image database in the future.