University of Oulu

Devangini Patel, X. Hong and G. Zhao, "Selective deep features for micro-expression recognition," 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, 2016, pp. 2258-2263. doi: 10.1109/ICPR.2016.7899972

Selective deep features for micro-expression recognition

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Author: Patel, Devangini1; Hong, Xiaopeng1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2016
Publish Date: 2019-08-07


Micro-expression recognition is a challenging task in computer vision field due to the repressed facial appearance and short duration. Previous work for micro-expression recognition have used hand-crafted features like LBP-TOP, Gabor filter and optical flow. This paper is the first work to explore the possible use of deep learning for micro-expression recognition task. Due to the lack of data for micro-expression, training a CNN model from micro-expression data is not feasible. Instead, transfer learning from objects and facial expressions based CNN models are used. The aim is to use feature selection to remove the irrelevant deep features for our task. This work extends evolutionary algorithms to search an optimal set of deep features so that it does not overfit the training data and generalizes well for the test data. Promising results are presented for various micro-expression datasets.

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ISBN: 978-1-5090-4847-2
ISBN Print: 978-1-5090-4848-9
Pages: 2258 - 2263
DOI: 10.1109/ICPR.2016.7899972
Host publication: 2016 Proceedings of 23rd Internaltional conference on Pattern Recognition (ICPR 2016)
Conference: International Conference on Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The authors would like to thank CSC Finland for providing computing resources. This work was sponsored by the Academy of Finland, Infotech Oulu and Tekes Fidipro program.
Copyright information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.