基于前后端交互的人脸识别系统

针对现有的人脸识别系统计算效率低和鲁棒性较差等问题,本文提出了一种基于前后端交互的人脸识别系统,系统包含客户端、数据库以及服务端.首先,在客户端提出了基于GrabCut的人脸兴趣区域(ROI)提取算法.其次,将提取到的ROI传输到服务端,并在服务端使用ResNet网络根据ROI提取人脸特征点.最后,将服务端中提取到的人脸特征点返回给客户端,在客户端将该信息与数据库中预存的特征点进行欧式距离匹配,得到人脸识别结果.实验在CeleA数据集与随机视频上进行测试,结果表明提出的ROI提取算法明显提升了人脸识别的精度和鲁棒性,并且系统的前后端交互结构相较于传统的非交互结构极大地提升了人脸识别的计算效率.

Face Recognition System Based on Front and Back Interaction

Aiming at the problems of low calculation efficiency and poor robustness of the existing face recognition system, this study proposed a face recognition system based on front and back interaction, including client, database, and server. First, a GrabCut-based facial Region Of Interest (ROI) extraction algorithm was proposed for the client end. Second, the extracted ROI is transmitted to the server, and the ResNet network is used on the server to extract facial feature points according to the ROI. Finally, the facial feature points extracted from the server were returned to the client, and the client performs Euclidean distance matching between this information and the feature points that were pre-stored in the database to obtain the face recognition result. The experiments were performed on the CeleA database and random videos, and the results show that the proposed ROI extraction algorithm significantly improves the accuracy and robustness of face recognition. Moreover, compared with the traditional non-interactive structure, the front and back interactive structure of the system greatly improves the computational efficiency of face recognition.