MSKFaceNet: A Lightweight Face Recognition Neural Network for Low-Power Devices

• Published in: IEEE access Vol. 13; pp. 120533 - 120546
• Main Authors: Zhang, Peng, Ma, Qinghua, Li, Yi, Cui, Min
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; attendance machine; Computational efficiency; Convolutional codes; Convolutional neural networks; Electronic devices; Embedded systems; Face recognition; Kernel; lightweight; local features; Modules; multi-scale; Neural networks; Parameterization; Performance evaluation; Power management; Real time; Real-time systems; Time and attendance systems; Transformers
• DOI: 10.1109/ACCESS.2025.3584814
• ISSN: 2169-3536

In recent years, the rapid development of lightweight convolutional neural networks (CNNs) and lightweight vision transformers (ViTs) has led to significant progress in the field of mobile computing. However, deploying facial recognition models on low-power devices (with power consumption below 10 watts) remains challenging. To address this issue, we designed a lightweight facial recognition network specifically optimized for low-power devices-MSKFaceNet (Multi-Scale Kernels Face Network). First, we propose a novel lightweight convolutional neural network module called MSKFNet. MSKFNet adopts a bottleneck design and introduces variable kernel convolutions from VarKNet, combined with channel shuffle and structural re-parameterization techniques, establishing an efficient CNN module for embedded systems. Built upon the MSKFNet module, MSKFaceNet further integrates a lightweight SE module to enhance its feature representation capabilities. Finally, we designed a real-time facial recognition attendance system based on MSKFaceNet and developed a prototype device. Experimental results show that MSKFaceNet, with only 0.54M parameters and 0.25 GFLOPS, achieves a recognition accuracy of 99.39% on the LFW dataset while delivering an inference speed of 10.8ms on the Jetson Nano platform. The proposed attendance system effectively and accurately performs facial recognition and attendance recording, significantly improving the efficiency and fairness of attendance management.