過去的人臉辨識技術常受環境影響使得辨識度不佳，現今使用基於深度學習的人臉辨識技術能夠克服如光影等環境造成的影響，但缺點是運算能力需求高且訓練辨識模型所需的時間太長。本論文提出一個運算能力需求相對低廉且能更快速完成訓練的方法，同時小幅的提升了辨識模型的準確度。
本論文在訓練模型時使用的損失函數是由LMCL改良而來，使得模型收斂更穩定且小幅提升了辨識模型的準確度。本論文改善了訓練方式使得模型收斂的速度提升約1.8倍，CNN模型則使用MobileNet的改良版本Mobilefacenet。

In the past, the performance of face recognition technology was not ideal because of the environmental influences. These days, the impact of environment such as light and shadow to face recognition has been overcome by the technology based on deep learning, but the disadvantages are the high computational requirement and the enormous time for training a CNN model. In this paper, a method for training models has been proposed which requires relatively low computational requirements, less training time but comes with higher accuracy.
The process of model convergence has become more stable and the model accuracy is a little raised due to the modification to the loss function-LMCL in this paper. There is a speedup about 1.8 times for model convergence because of the training method improved. The CNN model used in this paper is Mobilefacenet which is improved from MobileNet.

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