Citation

Khor, Huai Qian and See, John and Phan, Raphael Chung Wei and Lin, Weiyao (2018) Enriched Long-Term Recurrent Convolutional Network for Facial Micro-Expression Recognition. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 15-19 May 2018, Xi'an, China.

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Abstract

Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases. Recently, handcrafted techniques have achieved superior performance in micro-expression recognition but at the cost of domain specificity and cumbersome parametric tunings. In this paper, we propose an Enriched Long-term Recurrent Convolutional Network (ELRCN) that first encodes each micro-expression frame into a feature vector through CNN module(s), then predicts the micro-expression by passing the feature vector through a Long Short-term Memory (LSTM) module. The framework contains two different network variants: (1) Channel-wise stacking of input data for spatial enrichment, (2) Feature-wise stacking of features for temporal enrichment. We demonstrate that the proposed approach is able to achieve reasonably good performance, without data augmentation. In addition, we also present ablation studies conducted on the framework and visualizations of what CNN ”sees” when predicting the micro-expression classes

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Databases
Subjects:	Z Bibliography. Library Science. Information Resources > ZA3038-5190 Information resources (General) > ZA4050-4775 Information in specific formats or media > ZA4450-4460 Databases
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Mar 2021 22:02
Last Modified:	27 Mar 2021 22:02
URII:	http://shdl.mmu.edu.my/id/eprint/7549

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