Sensors, Vol. 20, Pages 6114: Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization

Sensors, Vol. 20, Pages 6114: Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization Sensors doi: 10.3390/s20216114 Authors: Hsiao-Chi Li Zong-Yue Deng Hsin-Han Chiang Despite considerable progress in face recognition technology in recent years, deep learning (DL) and convolutional neural networks (CNN) have revealed commendable recognition effects with the advent of artificial intelligence and big data. FaceNet was presented in 2015 and is able to significantly improve the accuracy of face recognition, while also being powerfully built to counteract several common issues, such as occlusion, blur, illumination change, and different angles of head pose. However, not all hardware can sustain the heavy computing load in the execution of the FaceNet model. In applications in the security industry, lightweight and efficient face recognition are two key points for facilitating the deployment of DL and CNN models directly in field devices, due to their limited edge computing capability and low equipment cost. To this end, this paper provides a lightweight learning network improved from FaceNet, which is called FN13, to break through the hardware limitation of constrained computational resources. The proposed FN13 takes the advantage of center loss to reduce the variations of the between-class features and enlarge the difference of the within-class features, instead of the triplet loss by using FaceNet. The resulting model...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research