دانلود مقاله انگلیسی رایگان:Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition - 2019
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  • Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition
    Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition


    ترجمه فارسی عنوان مقاله:

    Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition


    منبع:

    Sciencedirect - Elsevier - Computer Vision and Image Understanding, 184 (2019) 9-21: doi:10:1016/j:cviu:2019:04:003


    نویسنده:

    Weipeng Hu, Haifeng Hu∗


    چکیده انگلیسی:

    Heterogeneous face recognition (HFR) is still a challenging problem in computer vision community due to large appearance difference between near infrared (NIR) and visible light (VIS) modalities. Recently, breakthroughs have been made for traditional face recognition by applying deep learning on a huge amount of labeled VIS face samples. However, the same deep learning approach cannot be simply applied to HFR task due to large domain difference as well as insufficient pairwise images in different modalities during training. In general, the pooling layer of deep network can play the role of feature reduction, but also lead to the loss of useful face information, resulting in a decrease in the performance of HFR problem. It is important to eliminate modal-related information and retain more facial identity information. In this paper, we propose a novel method called Discriminant Deep Feature Learning Based on Joint Supervision Loss and Multi-layer Feature Fusion (DDFLJM) for HFR task. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new loss function called Scatter Loss (SL), which embeds both interand intra-class information for effectively training the deep model. To make full use of the various layers of the deep network, a Dimension Reduction Block (DRB) is designed to effectively extract the auxiliary features on multiple mid-level layers. An orthogonality constraint is introduced to the DRB block to reduce spectrum variations of two different modalities. The proposed SL is applied to multiple layers of network for joint supervision training, which enables multiple layers of the network to obtain discriminative identity features. Moreover, a Modified Gate Two-stream Neural Network (MGTNN) is adopted to fuse multiple-layer features. Extensive experiments are carried out on two challenging NIR-VIS HFR datasets CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
    Keywords: Heterogeneous face recognition | Deep learning | Joint supervision loss | Feature fusion


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 13
    حجم فایل: 1663 کیلوبایت

    قیمت: رایگان


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