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Symmetry 2019, 11(1), 52; https://doi.org/10.3390/sym11010052

Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition

1
Institute of Intelligent Information Processing, Taizhou University, Taizhou 318000, China
2
School of Software Engineering, Institute of Big Data Science and Industry, Taiyuan University, Shanxi 030006, China
3
College of information science and technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
4
College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
5
Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China
*
Author to whom correspondence should be addressed.
Received: 28 October 2018 / Revised: 28 December 2018 / Accepted: 30 December 2018 / Published: 5 January 2019
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Abstract

The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based facial expression owing to the gap between the visual descriptors and the emotions. In order to bridge the gap, a new video descriptor for facial expression recognition is presented to aggregate spatial and temporal convolutional features across the entire extent of a video. The designed framework integrates a state-of-the-art 30 stream and has a trainable spatial–temporal feature aggregation layer. This framework is end-to-end trainable for video-based facial expression recognition. Thus, this framework can effectively avoid overfitting to the limited emotional video datasets, and the trainable strategy can learn to better represent an entire video. The different schemas for pooling spatial–temporal features are investigated, and the spatial and temporal streams are best aggregated by utilizing the proposed method. The extensive experiments on two public databases, BAUM-1s and eNTERFACE05, show that this framework has promising performance and outperforms the state-of-the-art strategies. View Full-Text
Keywords: facial expression recognition; convolutional neural networks; temporal-spatial features; optical flow; feature aggregation facial expression recognition; convolutional neural networks; temporal-spatial features; optical flow; feature aggregation
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Pan, X.; Guo, W.; Guo, X.; Li, W.; Xu, J.; Wu, J. Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition. Symmetry 2019, 11, 52.

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