Research on a Face Recognition Algorithm Based on Joint Supervised Learning

Ruifang Zhang, Fenghua Dong, Tianyi Ji
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Abstract

∗Aiming at the problems of poor real-time capability and high hardware requirements of existing FaceNet face recognition model in actual application scenarios, a new SoftMax and Center Loss Function are used to replace the original Triplet Loss Function and jointly train the supervised face feature extraction model. The inception model is also improved. The simulation results show that the face feature extraction network designed in this paper has good robustness for the application scenarios of face recognition system and face occlusion scenarios.
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基于联合监督学习的人脸识别算法研究
*针对现有FaceNet人脸识别模型在实际应用场景中实时性差、硬件要求高的问题,采用新的SoftMax和Center Loss Function代替原有的Triplet Loss Function,共同训练有监督的人脸特征提取模型。初始模型也得到了改进。仿真结果表明,本文设计的人脸特征提取网络对于人脸识别系统应用场景和人脸遮挡场景都具有良好的鲁棒性。
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