An intelligent recognition framework of access control system with anti-spoofing function

Dongzhihan Wang; Guijin Ma; Xiaorui Liu; Dongzhihan Wang; Guijin Ma; Xiaorui Liu

doi:10.3934/math.2022585

AIMS Mathematics

2022, Volume 7, Issue 6: 10495-10512. doi: 10.3934/math.2022585

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Research article

An intelligent recognition framework of access control system with anti-spoofing function

1.
Automation School of Qingdao University, Qingdao 266071, China
2.
Shandong Key Laboratory of Industrial Control Technology, Qingdao 266071, China

Received: 19 December 2021 Revised: 04 March 2022 Accepted: 11 March 2022 Published: 28 March 2022
MSC : 68U10

Under the background that Covid-19 is spreading across the world, the lifestyle of people has to confront a series of changes and challenges. This also presents new problems and requirements to automation facilities. For example, nowadays masks have almost become necessities for people in public places. However, most access control systems (ACS) cannot recognize people wearing masks and authenticate their identities to deal with increasingly serious epidemic pressure. Consequently, many public entries have turned to an attendant mode that brings low efficiency, infection potential, and high possibility of negligence. In this paper, a new security classification framework based on face recognition is proposed. This framework uses mask detection algorithm and face authentication 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) and Reply-Attack datasets as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The average process time of a single frame is 0.12 seconds. 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 and face authentication task in real-time.
- face recognition,
- mask detection,
- spoofing detection,
- face authentication
Citation: Dongzhihan Wang, Guijin Ma, Xiaorui Liu. An intelligent recognition framework of access control system with anti-spoofing function[J]. AIMS Mathematics, 2022, 7(6): 10495-10512. doi: 10.3934/math.2022585

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Abstract

Under the background that Covid-19 is spreading across the world, the lifestyle of people has to confront a series of changes and challenges. This also presents new problems and requirements to automation facilities. For example, nowadays masks have almost become necessities for people in public places. However, most access control systems (ACS) cannot recognize people wearing masks and authenticate their identities to deal with increasingly serious epidemic pressure. Consequently, many public entries have turned to an attendant mode that brings low efficiency, infection potential, and high possibility of negligence. In this paper, a new security classification framework based on face recognition is proposed. This framework uses mask detection algorithm and face authentication 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) and Reply-Attack datasets as benchmarks. Performance evaluation indicates that the Half Total Error Rate (HTER) is 9.7%, the Equal Error Rate (EER) is 5.5%. The average process time of a single frame is 0.12 seconds. 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 and face authentication task in real-time.

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