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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2020

23.10.2019 | Original Article

A discriminative deep association learning for facial expression recognition

verfasst von: Xing Jin, Wenyun Sun, Zhong Jin

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2020

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Abstract

Deep learning based facial expression recognition becomes more successful in many applications. However, the lack of labeled data is still a bottleneck for better recognition performance. Thus, it is of practical significance to exploit the rich unlabeled data for training deep neural networks (DNNs). In this paper, we propose a novel discriminative deep association learning (DDAL) framework. The unlabeled data is provided to train the DNNs with the labeled data simultaneously, in a multi-loss deep network based on association learning. Moreover, the discrimination loss is also utilized to ensure intra-class clustering and inter-class centers separating. Furthermore, a large synthetic facial expression dataset is generated and used as unlabeled data. By exploiting association learning mechanism on two facial expression datasets, competitive results are obtained. By utilizing synthetic data, the performance is increased clearly.
Vorheriger Artikel A deep neural networks based recommendation algorithm using user and item basic data
Nächster Artikel A technical view on neural architecture search

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Metadaten
Titel
A discriminative deep association learning for facial expression recognition
verfasst von
Xing Jin
Wenyun Sun
Zhong Jin
Publikationsdatum
23.10.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01024-2

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