The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Audio-Visual Emotion Recognition in Video Clips
Open accessAbstract
This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features are extracted. For the visual part, two strategies are considered. First, facial landmarks' geometric relations, i.e., distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames, which are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to key-frames summarizing videos. Finally, confidence outputs of all the classifiers from all the modalities are used to define a new feature space to be learned for final emotion label prediction, in a late fusion/stacking fashion. The experiments conducted on the SAVEE, eNTERFACE'05, and RML databases show significant performance improvements by our proposed system in comparison to current alternatives, defining the current state-of-the-art in all three databases.
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The eNTERFACE’05 Audio-Visual Emotion Database
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Cited by
Survey on Emotional Body Gesture Recognition
Noroozi, F., Corneanu, C. A., Kaminska, D., Sapinski, T., Escalera, S., & Anbarjafari, G. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2874986 · Cited by 455
Self-citationAutomated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142
Chen, L., Wang, K., Li, M., Wu, M., Pedrycz, W., & Hirota, K. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3150097 · Cited by 113
A hybrid latent space data fusion method for multimodal emotion recognition
Nemati, S., Rohani, R., Basiri, M. E., Abdar, M., Yen, N. Y., & Makarenkov, V. (2019) · IEEE Access · doi:10.1109/access.2019.2955637 · Cited by 98
Metadata
- Authors
- Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S., & Anbarjafari, G.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2019-01-01
- Volume
- 10
- Issue
- 1
- Pages
- 60-75
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2624340939
- Cited by
- 266
- References
- 52
- FWCI
- 25.48
- Type
- journal article
- Language
- en
- Open access
- green
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