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Sensors 2018, 18(8), 2666; https://doi.org/10.3390/s18082666

Fine-Grained Face Annotation Using Deep Multi-Task CNN

Department of Informatics, Systems and Communication, University of Milano-Bicocca, viale Sarca, 336 Milano, Italy
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Received: 3 July 2018 / Revised: 6 August 2018 / Accepted: 13 August 2018 / Published: 14 August 2018
(This article belongs to the Section Intelligent Sensors)
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Abstract

We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN’s parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations’ sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach. View Full-Text
Keywords: face analysis; convolutional neural networks; multi-task learning; gender recognition; age group recognition; face attributes’ estimation face analysis; convolutional neural networks; multi-task learning; gender recognition; age group recognition; face attributes’ estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Celona, L.; Bianco, S.; Schettini, R. Fine-Grained Face Annotation Using Deep Multi-Task CNN. Sensors 2018, 18, 2666.

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