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• W2896912191 abstract "In this article, a two-stage refinement network is proposed for facial landmarks detection on unconstrained conditions. Our model can be divided into two modules, namely the Head Attribude Classifier (HAC) module and the Domain-Specific Refinement (DSR) module. Given an input facial image, HAC adopts multi-task learning mechanism to detect the head pose and obtain an initial shape. Based on the obtained head pose, DSR designs three different CNN-based refinement networks trained by specific domain, respectively, and automatically selects the most approximate network for the landmarks refinement. Different from existing two-stage models, HAC combines head pose prediction with facial landmarks estimation to improve the accuracy of head pose prediction, as well as obtaining a robust initial shape. Moreover, an adaptive sub-network training strategy applied in the DSR module can effectively solve the issue of traditional multi-view methods that an improperly selected sub-network may result in alignment failure. The extensive experimental results on two public datasets, AFLW and 300W, confirm the validity of our model." @default.
• W2896912191 created "2018-10-26" @default.
• W2896912191 creator A5029203943 @default.
• W2896912191 creator A5067937579 @default.
• W2896912191 date "2018-10-10" @default.
• W2896912191 modified "2023-09-26" @default.
• W2896912191 title "Joint Head Attribute Classifier and Domain-Specific Refinement Networks for Face Alignment" @default.
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• W2896912191 doi "https://doi.org/10.1145/3241059" @default.
• W2896912191 hasPublicationYear "2018" @default.
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