Human face recognition by adaptive processing of tree structures representation

Cho, Siu-Yeung; Wong, Jia-Jun

doi:10.1007/s00521-007-0108-8

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Accordingly, borrowing the prediction of other tasks to help the prediction of a given task is natural. For example, in the face attribute prediction problem, given an image, the prediction of female gender and wearing long hair is usually related (Cho and Wong, 2008;Owusu, Zhan and Mao, 2014;Wong and Cho, 2010;Yao, Chen, Jia and Liu, 2018;Zhong, Sullivan and Li, 2016).…”

Section: Backgroundsmentioning

confidence: 99%

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Shang

Wang³

2019

Computational Intelligence and Neuroscience

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In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark datasets of multiple face attribute prediction, multitask natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multitask problems.

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Section: Backgroundsmentioning

confidence: 99%

Low-Rank Deep Convolutional Neural Network for Multitask Learning

Shang

Wang³

2019

Computational Intelligence and Neuroscience

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“…To date, we have looked into the several aspects of facial expression recognition which are published in separate publications (Cho et al, 2007;2008;. The achieved developments thus far include the unsupervised learning of facial emotion categorization, the tree structured model of classification and the deployment of the system in hand-held mobile devices.…”

Section: Future Trends and Conclusionmentioning

confidence: 99%