Primary Study on the Face-recognition Framework with anti-spoofing function
Xiao Wu, Dekang Zhang, Xiaorui Liu · 2022
In pracyical life scene, most access control systems (ACS) cannot solve the problem of various sproofing ways and mask-wearing recognition. In this paper, a new security classification framework based on face recognition is proposed. This framework uses face recognition algorithm with anti-spoofing function. In order to evaluate the performance of the framework, this paper employs the Chinese Academy of Science Institute of Automation-Face Anti-spoofing Datasets (CASIA-FASD) as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The results demonstrate that this framework has a high anti-spoofing capability and can be employed on the embedded system to complete the mask detection in real-time.