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• W2969703002 abstract "In this paper, we propose a Face Segmentor-Enhanced Network (FSENet) for face recognition to exploit facial localized property. Most existing methods emphasize the holistic characteristics on entire face images, which have limit discriminative ability due to large intra-class variations and inter-class fine-grain. To address this, we present a face segmentor to parse the face into local components and explore their internal correlations, which strengthens the discriminability to discern identities. Specifically, we introduce a semantic parsing module to assign each pixel with a semantic part label. This module generates a set of parsing maps, where each of them represents the pixel-wise occurrence probability of a certain facial component. We then segment facial regions masked by the parsing maps to achieve local features. We further build the structure correlation of facial part features to boost personalized attribute. We finally incorporate holistic and local information to improve the discriminative power of the face descriptor. Extensive experiments on popular public-domain datasets including Labeled Face in the Wild (LFW), Youtube Faces (YTF), IARPA IJB-A, IJB-B and IJB-C, and the MegaFace Challenge show that our method achieves promising performance." @default.
• W2969703002 created "2019-08-29" @default.
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• W2969703002 date "2019-10-01" @default.
• W2969703002 modified "2023-10-16" @default.
• W2969703002 title "Face Segmentor-Enhanced Deep Feature Learning for Face Recognition" @default.
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• W2969703002 doi "https://doi.org/10.1109/tbiom.2019.2936624" @default.
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