Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
Abstract
Emotion recognition is challenging due to the emotional gap between emotions and audio-visual features. Motivated by the powerful feature learning ability of deep neural networks, this paper proposes to bridge the emotional gap by using a hybrid deep model, which first produces audio-visual segment features with Convolutional Neural Networks (CNNs) and 3D-CNN, then fuses audio-visual segment features in a Deep Belief Networks (DBNs). The proposed method is trained in two stages. First, CNN and 3D-CNN models pre-trained on corresponding large-scale image and video classification tasks are fine-tuned on emotion recognition tasks to learn audio and visual segment features, respectively. Second, the outputs of CNN and 3D-CNN models are combined into a fusion network built with a DBN model. The fusion network is trained to jointly learn a discriminative audio-visual segment feature representation. After average-pooling segment features learned by DBN to form a fixed-length global video feature, a linear Support Vector Machine is used for video emotion classification. Experimental results on three public audio-visual emotional databases, including the acted RML database, the acted eNTERFACE05 database, and the spontaneous BAUM-1s database, demonstrate the promising performance of the proposed method. To the best of our knowledge, this is an early work fusing audio and visual cues with CNN, 3D-CNN, and DBN for audio-visual emotion recognition.
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Knowledge transfer from domains like music and visual object recognition is also possible.
“Knowledge transfer from domains like music [141] and visual object recognition [132], [85] is also possible.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [85]
Some studies considered using known architectures from object recognition and regarded it as necessary to pre-train these larger models on ImageNet to avoid overfitting the relatively few labeled examples.
“Some considered using known architectures from object recognition [132], [85], [127]. Here, the authors regarded it as necessary to pre-train these larger models on the ImageNet database to avoid overfitting the relatively few labeled examples.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [85]
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- Authors
- Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q.
- Affiliations
- Peking University (CN)
- The University of Texas at San Antonio (US)
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2018-10
- Volume
- 28
- Issue
- 10
- Pages
- 3030-3043
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W2703895418
- Semantic Scholar
- 6aeadade814902ddb9d7fd0e1d2d64a70f0e3ff9
- Cited by
- 346
- References
- 69
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 39 · 2023: 45 · 2022: 57 · 2021: 55 · 2020: 43 · 2019: 43 · 2018: 20 · 2017: 1
- FWCI
- 38.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Circuits and Systems for Video Technology (publishedVersion)
- Funders
- National Natural Science Foundation of China (61572050, LY16F020011, 91538111, 61620106009, 61429201)
- ARO (W911NF-15-1-0290)
- Faculty Research Gift Awards by NEC Laboratories of America and Blippar
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- audio-visual emotion recognition, affective features, 3D convolutional neural networks, deep belief network, audio-visual feature fusion, support vector machine classification
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