Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

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Face attributes prediction has an increasing amount of applications in human-computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.
Publisher
MDPI
Issue Date
2019-06
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.9, no.12

ISSN
2076-3417
DOI
10.3390/app9122535
URI
http://hdl.handle.net/10203/263748
Appears in Collection
EE-Journal Papers(저널논문)
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