A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation
AbstractAccurate 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. View Full-Text
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Khan, K.; Attique, M.; Syed, I.; Sarwar, G.; Irfan, M.A.; Khan, R.U. A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation. Entropy 2019, 21, 647.
Khan K, Attique M, Syed I, Sarwar G, Irfan MA, Khan RU. A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation. Entropy. 2019; 21(7):647.Chicago/Turabian Style
Khan, Khalil; Attique, Muhammad; Syed, Ikram; Sarwar, Ghulam; Irfan, Muhammad A.; Khan, Rehan U. 2019. "A Unified Framework for Head Pose, Age and Gender Classification through End-to-End Face Segmentation." Entropy 21, no. 7: 647.
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