The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics
Abstract
As a severe psychiatric disorder disease, depression is a state of low mood and aversion to activity, which prevents a person from functioning normally in both work and daily lives. The study on automated mental health assessment has been given increasing attentions in recent years. In this paper, we study the problem of automatic diagnosis of depression. A new approach to predict the Beck Depression Inventory II (BDI-II) values from video data is proposed based on the deep networks. The proposed framework is designed in a two stream manner, aiming at capturing both the facial appearance and dynamics. Further, we employ joint tuning layers that can implicitly integrate the appearance and dynamic information. Experiments are conducted on two depression databases, AVEC2013 and AVEC2014. The experimental results show that our proposed approach significantly improve the depression prediction performance, compared to other visual-based approaches.
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003) · JAMA · doi:10.1001/jama.289.23.3095 · Cited by 7,988
Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients
Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996) · Journal of Personality Assessment · doi:10.1207/s15327752jpa6703_13 · Cited by 5,965
Face Description with Local Binary Patterns: Application to Face Recognition
Ahonen, T., Hadid, A., & Pietikainen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,584
Learning realistic human actions from movies
Laptev, I., Marszalek, M., Schmid, C., & Rozenfeld, B. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587756 · Cited by 3,444
Dlib-ml: A Machine Learning Toolkit
King, D. E. (2009) · doi:10.5555/1577069.1755843 · Cited by 2,777
High Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,570
Belmaker, R. H., & Agam, G. (2008) · New England Journal of Medicine · doi:10.1056/nejmra073096 · Cited by 2,166
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905 · cited 2×
“Partial Least Squares (PLS) regression is applied for the final prediction.”
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Deep learning in mental health outcome research: a scoping review
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368
Deep learning for depression recognition with audiovisual cues: A review
He, L., Niu, M., Tiwari, P., Marttinen, P., Su, R., Jiang, J., Guo, C., Wang, H., Ding, S., Wang, Z., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.10.012 · Cited by 253
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171
Fan, H., Zhang, X., Xu, Y., Fang, J., Zhang, S., Zhao, X., & Yu, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102161 · Cited by 137
Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11100957 · Cited by 52
Metadata
- Authors
- Zhu, Y., Shang, Y., Shao, Z., & Guo, G.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2018-10-01
- Volume
- 9
- Issue
- 4
- Pages
- 578-584
- ISSN
- 1949-3045 (ISSN-L)
- 2371-9850
- OpenAlex
- W2570412145
- Cited by
- 257
- References
- 33
- FWCI
- 25.48
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhu, Y., Shang, Y., Shao, Z., & Guo, G. (2018). Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2017.2650899
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