The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Open accessAbstract
Mood disorders are inherently related to emotion. In particular, the behaviour of people suffering from mood disorders such as unipolar depression shows a strong temporal correlation with the affective dimensions valence and arousal. In addition, psychologists and psychiatrists take the observation of expressive facial and vocal cues into account while evaluating a patient's condition. Depression could result in expressive behaviour such as dampened facial expressions, avoiding eye contact, and using short sentences with flat intonation. It is in this context that we present the third Audio-Visual Emotion recognition Challenge (AVEC 2013). The challenge has two goals logically organised as sub-challenges: the first is to predict the continuous values of the affective dimensions valence and arousal at each moment in time. The second sub-challenge is to predict the value of a single depression indicator for each recording in the dataset. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.
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Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
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Self-citationThe INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938
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Self-citationDetecting depression from facial actions and vocal prosody
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OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
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Self-citationAutomatic behavior descriptors for psychological disorder analysis
Scherer, S., Stratou, G., Mahmoud, M., Boberg, J., Gratch, J., Rizzo, A., & Morency, L.-P. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553789 · Cited by 147
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Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Sariyanidi, E., Gunes, H., & Cavallaro, A. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366127 · Cited by 685
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge
Valstar, M., Gratch, J., Schuller, B., Ringeval, F., Lalanne, D., Torres Torres, M., Scherer, S., Stratou, G., Cowie, R., & Pantic, M. (2016) · Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2988257.2988258 · Cited by 503
Self-citationEEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508
Metadata
- Authors
- Valstar, M., Schuller, B., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantic, M.
- Venue
- Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
- Publisher
- ACM
- Published
- 2013-10-21
- Pages
- 3-10
- OpenAlex
- W1976066595
- Cited by
- 581
- References
- 9
- FWCI
- 53.59
- Type
- conference paper
- Language
- fr
- Open access
- green
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- Valstar, M., Schuller, B., Smith, K., Eyben, F., Jiang, B., Bilakhia, S., Schnieder, S., Cowie, R., & Pantic, M. (2013). AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge. https://doi.org/10.1145/2512530.2512533
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