Impact Analysis of Different Effective Loss Functions by Using Deep Convolutional Neural Network for Face Recognition

Abstract

Smart Library Automation System is increasingly attractive as an effective digital library system. Adapting automation in the library helps reduce the duplication of work, time-saving, and boosts work efficiency. One such significant feature is face recognition integrated. With this application, the system can use face recognition to enter and get the details of an end user. In recent years, face recognition has achieved many prodigious accomplishments based on Deep Convolutional Neural Networks (DCNN). In addition to constructing large face datasets, designing new effective DCNN architectures and loss functions are two trends to improve the performance of face recognition systems. Therefore, many studies have been published in state-of-the-art methods, making high-accuracy face recognition systems more possible than days in the past. However, it is still difficult for all research communities to train robust face recognition models because it depends heavily on their resources. This paper investigates and analyzes the effect of several effective loss functions based on softmax. Moreover, we also evaluate how hyper-parameter settings can impact the optimization process as well as the final recognition performance of the model trained by re-implementing these methods. The results of our experiments achieve state-of-the-art figures, which show the proposed method’s massive potential in improving face recognition performance.

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