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  • Open Access

    ARTICLE

    Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

    Mostafa Diba*, Hossein Khosravi

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590

    Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories of feature maps, the reduction… More >

  • Open Access

    REVIEW

    Survey on the Loss Function of Deep Learning in Face Recognition

    Jun Wang1, Suncheng Feng2,*, Yong Cheng3, Najla Al-Nabhan4

    Journal of Information Hiding and Privacy Protection, Vol.3, No.1, pp. 29-45, 2021, DOI:10.32604/jihpp.2021.016835

    Abstract With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss. In this paper, based… More >

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