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Multi-Attribute Recognition of Facial Images Considering Exclusive and Correlated Relationship Among Attributes

1
School of Computer Science and Engineering, Kyungpook National University, Daegu 702-701, Korea
2
Department of Statistics, Kyungpook National University, Daegu 702-701, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2034; https://doi.org/10.3390/app9102034
Received: 6 April 2019 / Revised: 8 May 2019 / Accepted: 15 May 2019 / Published: 17 May 2019
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

Multi-attribute recognition is one of the main topics attaining much attention in the pattern recognition field these days. The conventional approaches to multi-attribute recognition has mainly focused on developing an individual classifier for each attribute. However, due to rapid growth of deep learning techniques, multi-attribute recognition using multi-task learning enables the simultaneous recognition of more than two relevant recognition tasks through a single network. A number of studies on multi-task learning have shown that it is effective in improving recognition performance for all tasks when related tasks are learned together. However, since there are no specific criteria for determining the relationship among attributes, it is difficult and confusing to choose a good combination of tasks that have a positive impact on recognition performance. As one way to solve this problem, we propose a multi-attribute recognition method based on the novel output representations of a deep learning network which automatically learns the exclusive and joint relationship among attribute recognition tasks. We apply our proposed method to multi-attribute recognition of facial images, and confirm the effectiveness through experiments on a benchmark database. View Full-Text
Keywords: multi-attribute recognition; multi-task learning; facial attributes; joint probability distribution; relationship among attributes multi-attribute recognition; multi-task learning; facial attributes; joint probability distribution; relationship among attributes
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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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Hyun, C.; Seo, J.; Lee, K.E.; Park, H. Multi-Attribute Recognition of Facial Images Considering Exclusive and Correlated Relationship Among Attributes. Appl. Sci. 2019, 9, 2034.

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