A High-Accuracy and Faster Face Recognizer Supporting Biometric Continuous Authentication for Smart Factory Workers
Adri Priadana, Duy-Linh Nguyen, Xuan-Thuy Vo, Muhamad Dwisnanto Putro, Ge Cao, Kang-Hyun Jo · IEEE Transactions on Industrial Informatics · 2025
Smart factories require secure and sustainable worker authentication for safe operations. Biometric continuous authentication based on facial recognition is one of the most convenient mechanisms. This method applies a face recognition task to verify the captured face as an authorized user. However, existing methods that employ large networks for high-accuracy face recognition incur high computational costs and slow down the process, rendering them unsuitable for continuous operation. This work proposes an efficient and rapid face recognizer with high accuracy. It offers a faster face residual network, containing efficient FasterFace blocks and efficient channel spatial attention for improved feature extraction. As a result, the proposed network achieves 97.08% based on average accuracy, outperforming the other networks on five benchmark datasets. It performs faster at 19.91 frames per second in real time on CPU-based hardware when integrated with a face detector, showcasing its capacity to support real-time biometric continuous authentication for smart factory workers.