Multi-face recognition and dynamic tracking based on reinforcement learning algorithm

Aiming at the problem that the current low accuracy rate of face detection and target tracking, a reinforcement learning algorithm is proposed, which integrates face detection technology and target tracking technology organically, adopts the face detection algorithm based on Multi-Task Convolutional Neural Network (MTCNN) and target tracking algorithm based on Kalman filtering, so as to realize face detection, multiplayer face recognition and dynamic tracking of personnel movement. In this paper, the configuration environment is Anaconda, the operating platform is PyCharm, the video-based face detection and dynamic capture and rapid identification system has been designed and developed. The system consists of two modules: face detection module and target tracking module. The optimized face detection and dynamic capture algorithm improved the detection success rate by about 11.5%, the face detection success rate by about 15.2%, the dynamic capture success rate increased by about 12.0%, and the optimized system has a wider practicality.

Abstract licence: CC BY

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