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

Abstract

<abstract> <p>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.</p> </abstract>

Adaptive composite frequency control of power systems using reinforcement learning

“…In future work, we may improve reinforcement learning algorithms using different NN structures (such as those reported in ref. [42,43]) to address complicated non-linear conditions…”

“…In future work, we may improve reinforcement learning algorithms using different NN structures (such as those reported in ref. [42, 43]) to address complicated non‐linear conditions while considering event‐triggered control to cope with limited communication resources.…”

Actuation delay compensation of robots in semi-physical test

“…Realizing high performance of ordinary robots is one of the core problems in robotic research ( Chen and Qiao, 2020a , Qiao et al, 2022b ). With the development of robot application and the combination of deep learning technology and robots ( Qi and Su, 2022 ), robots are increasingly intelligent ( Chen and Qiao, 2020b , Wang et al, 2022 ) and behaving more and more like human beings ( Su et al, 2022b ). Now robots can be driven by humans to display natural and appropriate behaviors in social scenes ( Liu et al, 2022a ).…”

Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

“…Deep learning-related technologies are increasingly integrated into people’s daily life, and object detection algorithms ( Qi et al, 2021 ; Liu et al, 2022a , b ; Xu et al, 2022 ), as a crucial component of the autonomous driving perception layer, can create a solid foundation for behavioral judgments during autonomous driving. Although object detection algorithms based on 2D images ( Bochkovskiy et al, 2020 ; Bai et al, 2022 ; Cheon et al, 2022 ; Gromada et al, 2022 ; Long et al, 2022 ; Otgonbold et al, 2022 ; Wahab et al, 2022 ; Wang et al, 2022 ) have had a lot of success at this stage, single-view images cannot completely reflect the position pose, and motion orientation of objects in 3D space due to the lack of depth information in 2D images. Consequently, in the field of autonomous driving, the focus of object detection research has increasingly switched from 2D image detection to 3D image detection and point cloud detection.…”