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• W2954672586 abstract "Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results." @default.
• W2954672586 created "2019-07-12" @default.
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• W2954672586 date "2019-06-30" @default.
• W2954672586 modified "2023-10-17" @default.
• W2954672586 title "A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation" @default.
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• W2954672586 doi "https://doi.org/10.3390/e21070647" @default.
• W2954672586 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7515140" @default.
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• W2954672586 hasPublicationYear "2019" @default.
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