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Deep learning models for facial expression recognition

conference contribution
posted on 2018-01-01, 00:00 authored by Atul SajjanharAtul Sajjanhar, ZhaoQi Wu, Quan Wen
We investigate facial expression recognition using state-of-the-art classification models. Recently, CNNs have been extensively used for face recognition. However, CNNs have not been thoroughly evaluated for facial expression recognition. In this paper, we train and test a CNN model for facial expression recognition. The performance of the CNN model is used as benchmark for evaluating other pre-trained deep CNN models. We evaluate the performance of Inception and VGG which are pre-trained for object recognition, and compare these with VGG-Face which is pre-trained for face recognition. All experiments are performed on publicly available face databases, namely, CK+, JAFFE and FACES.

History

Event

Australian Pattern Recognition Society. Conference (2018 : Canberra, A.C.T.)

Series

Australian Pattern Recognition Society Conference

Pagination

1 - 6

Publisher

Institute of Electrical and Electronics Engineers

Location

Canberra, A.C.T.

Place of publication

Piscataway, N.J.

Start date

2018-12-10

End date

2018-12-13

ISBN-13

978-1-5386-6602-9

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

M Murshed, M Paul, M Asikuzzaman, M Pickering, A Natu, A Robles-Kelly, S You, L Zheng, A Rahman

Title of proceedings

DICTA 2018 : Proceedings of the 2018 International Conference on Digital Image Computing: Techniques and Applications