Abstract
Video-based facial expression recognition is a very challenging task. The expression features portrayed by traditional ResNet18 are not rich enough, while the classical method LSTM to process expression videos may not extract effective temporal features for cases with weaker emotion intensity. This paper proposes a spatiotemporal attention network to extract more diverse spatial features and more effective temporal relationships. Firstly, a spatial attention module is used to enhance the expression features extracted by the ResNet18 and remove redundancy. Then, multiple levels of information are combined to extract richer expression features. Meanwhile, the video stream is divided into a series of video clips using a sliding window, and the temporal features are extracted from each small clip by the LSTM, which is a simple but effective way to divide the video. Third, considering the different importance of expression features extracted from each window for the results of expression recognition of the whole video, an attention-based score fusion module is proposed to fuse expression information from multiple windows. We perform comprehensive experiments on in-the-wild FER benchmarks (AFEW8.0 and HUST-MM). Quantitative and qualitative analyses demonstrate the effectiveness of our proposed method.
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Cao, Z., Chu, Z., Liu, D., et al.: A vector-based representation to enhance head pose estimation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1188–1197 (2021)
Cao, Z., Liu, D., Wang, Q., et al.: Towards unbiased label distribution learning for facial pose estimation using anisotropic spherical gaussian. arXiv preprint arXiv:2208.09122 (2022)
Chen, L., Zhou, M., Su, W., et al.: Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction. Inf. Sci. 428, 49–61 (2018)
Choi, D.Y., Song, B.C.: Semi-supervised learning for facial expression-based emotion recognition in the continuous domain. Multimed. Tools Appl. 79(37), 28,169-28,187 (2020)
Cui, Y., Yan, L., Cao, Z., et al.: Tf-blender: Temporal feature blender for video object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8138–8147 (2021)
Dhall, A., Goecke, R., Lucey, S., et al.: Collecting large, richly annotated facial-expression databases from movies. IEEE Multimed. 19(03), 34–41 (2012)
Ding, H., Zhou, S.K., Chellappa, R.: Facenet2expnet: Regularizing a deep face recognition net for expression recognition. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), IEEE, pp. 118–126 (2017)
Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Fu, J., Zheng, H., Mei, T.: Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4438–4446 (2017)
Gao, J., Zhao, Y.: Tfe: A transformer architecture for occlusion aware facial expression recognition. Frontiers in Neurorobotics 15 (2021)
Gogić, I., Manhart, M., Pandžić, I.S., et al.: Fast facial expression recognition using local binary features and shallow neural networks. Vis. Comput. 36(1), 97–112 (2020). https://doi.org/10.1007/s00371-018-1585-8
Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE international conference on computer vision workshops, pp. 3154–3160 (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
Hu, J., Liao, X., Wang, W., et al.: Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolutional network. IEEE Trans. Circ. Syst. Video Technol. 32(3), 1089–1102 (2021)
Hu, M., Wang, H., Wang, X., et al.: Video facial emotion recognition based on local enhanced motion history image and cnn-ctslstm networks. J. Vis. Commun. Image Represent. 59, 176–185 (2019)
Hu, M., Ge, P., Wang, X., et al.: A spatio-temporal integrated model based on local and global features for video expression recognition. Vis. Comput. 38(8), 2617–2634 (2022). https://doi.org/10.1007/s00371-021-02136-z
Huang, Q., Huang, C., Wang, X., et al.: Facial expression recognition with grid-wise attention and visual transformer. Inf. Sci. 580, 35–54 (2021)
Ji, S., Xu, W., Yang, M., et al.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2012)
Jiang, X., Zong, Y., Zheng, W., et al.: Dfew: A large-scale database for recognizing dynamic facial expressions in the wild. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2881–2889 (2020)
Jung, H., Lee, S., Yim, J., et al.: Joint fine-tuning in deep neural networks for facial expression recognition. In: Proceedings of the IEEE international conference on computer vision, pp. 2983–2991 (2015)
Kim, D.H., Lee, M.K., Choi, D.Y., et al.: Multi-modal emotion recognition using semi-supervised learning and multiple neural networks in-the-wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 529–535 (2017)
Kumar, V., Rao, S., Yu, L.: Noisy student training using body language dataset improves facial expression recognition. In: European Conference on Computer Vision, Springer, pp. 756–773 (2020)
Li, K., Jin, Y., Akram, M.W., et al.: Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. Vis. Comput. 36(2), 391–404 (2020). https://doi.org/10.1007/s00371-019-01627-4
Li, Y., Zeng, J., Shan, S., et al.: Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Trans. Image Process. 28(5), 2439–2450 (2018)
Liang, D., Liang, H., Yu, Z., et al.: Deep convolutional bilstm fusion network for facial expression recognition. Vis. Comput. 36(3), 499–508 (2020). https://doi.org/10.1007/s00371-019-01636-3
Liang, X., Xu, L., Zhang, W., et al.: A convolution-transformer dual branch network for head-pose and occlusion facial expression recognition. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02413-5
Liao, X., Li, K., Zhu, X., et al.: Robust detection of image operator chain with two-stream convolutional neural network. IEEE J. Sel. Top. Signal Process. 14(5), 955–968 (2020)
Liu, D., Cui, Y., Tan, W., et al.: (2021a) Sg-net: Spatial granularity network for one-stage video instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9816–9825
Liu, D., Cui, Y., Yan, L., et al.: Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6101–6109 (2021b)
Mavani, V., Raman, S., Miyapuram, K.P.: Facial expression recognition using visual saliency and deep learning. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2783–2788 (2017)
Meng, D., Peng, X., Wang, K., et al.: Frame attention networks for facial expression recognition in videos. In: 2019 IEEE International Conference on Image Processing (ICIP), IEEE, pp. 3866–3870 (2019)
Müller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? Adv. Neural Inform. process. syst. 32 (2019)
Ouyang, X., Kawaai, S., Goh, E.G.H., et al.: Audio-visual emotion recognition using deep transfer learning and multiple temporal models. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 577–582 (2017)
Peng, Y., He, X., Zhao, J.: Object-part attention model for fine-grained image classification. IEEE Trans. Image Process. 27(3), 1487–1500 (2017)
Salgado, P., Banos, O., Villalonga, C.: Facial expression interpretation in asd using deep learning. In: International Work-Conference on Artificial Neural Networks, pp. 322–333. Springer (2021)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)
Sun, N., Li, Q., Huan, R., et al.: Deep spatial-temporal feature fusion for facial expression recognition in static images. Pattern Recogn. Lett. 119, 49–61 (2019)
Tan, J., Liao, X., Liu, J., et al.: Channel attention image steganography with generative adversarial networks. IEEE Trans. Network Sci. Eng. 9(2), 888–903 (2021)
Vielzeuf, V., Pateux, S., Jurie, F.: Temporal multimodal fusion for video emotion classification in-the-wild. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 569–576 (2017)
Wang, H., Zhou, G., Hu, M., et al.: Video emotion recognition using local enhanced motion history image and cnn-rnn networks. In: Chinese Conference on Biometric Recognition, pp. 109–119. Springer (2018)
Wang, K., Peng, X., Yang, J., et al.: Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans. Image Process. 29, 4057–4069 (2020)
Wen, Z., Lin, W., Wang, T., et al.: Distract your attention: multi-head cross attention network for facial expression recognition. arXiv preprint arXiv:2109.07270 (2021)
Woo, S., Park, J., Lee, J.Y., et al.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Yan, L., Ma, S., Wang, Q., et al.: Video captioning using global-local representation. IEEE Trans. Circ. Syst. Video Technol. 32(10), 6642–6656 (2022). https://doi.org/10.1109/TCSVT.2022.3177320
Yan, L., Wang, Q., Cui, Y., et al.: Gl-rg: Global-local representation granularity for video captioning. arXiv preprint arXiv:2205.10706 (2022b)
Zhang, C.B., Jiang, P.T., Hou, Q., et al.: Delving deep into label smoothing. IEEE Trans. Image Process. 30, 5984–5996 (2021)
Zhang, K., Huang, Y., Du, Y., et al.: Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Trans. Image Process. 26(9), 4193–4203 (2017)
Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
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This study was supported by the National Key R &D Program of China under Grant 2020YFC0833102.
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Yi, Y., Xu, Y., Ye, Z. et al. STAN: spatiotemporal attention network for video-based facial expression recognition. Vis Comput 39, 6205–6220 (2023). https://doi.org/10.1007/s00371-022-02721-w
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DOI: https://doi.org/10.1007/s00371-022-02721-w