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Enhanced Depression Detection from Facial Cues Using Univariate Feature Selection Techniques

  • Swati RathiEmail author
  • Baljeet Kaur
  • R. K. Agrawal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)

Abstract

Timely detection of depression and the accurate assessment of its severity are the two major challenges that face the medical community. To assist the clinicians, various objective measures are being explored by researchers. In literature, features extracted from the images or videos, are found relevant for detection of depression. Various feature extraction methods are suggested in literature. However, the high dimensionality of the features so obtained provide an overfitted learning model. This is handled in this work with the help of three popular univariate filter feature selection methods, which identify the reduced size of relevant subset of features. The combinations of univariate techniques with well-known classification and regression techniques are investigated. The performance of classification and regression techniques improved with the use of feature selection methods. Moreover, the proposed model has outperformed most of the video-based existing methods for identifying depression and determining its level of severity.

Keywords

Classification Depression Motion History Image Regression Univariate feature selection Visual features 

References

  1. 1.
  2. 2.
    Al Jazaery, M., Guo, G.: Video-based depression level analysis by encoding deep spatiotemporal features. IEEE Trans. Affect. Comput. (2018).  https://doi.org/10.1109/TAFFC.2018.2870884
  3. 3.
    Bellman, R.: Curse of Dimensionality. Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton (1961)CrossRefGoogle Scholar
  4. 4.
    Cohn, J.F., et al.: Detecting depression from facial actions and vocal prosody. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–7. IEEE (2009)Google Scholar
  5. 5.
    Cummins, N., Joshi, J., Dhall, A., Sethu, V., Goecke, R., Epps, J.: Diagnosis of depression by behavioural signals: a multimodal approach. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, pp. 11–20. ACM (2013)Google Scholar
  6. 6.
    Dibeklioğlu, H., Hammal, Z., Yang, Y., Cohn, J.F.: Multimodal detection of depression in clinical interviews. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 307–310. ACM (2015)Google Scholar
  7. 7.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinf. Comput. Biol. 3(02), 185–205 (2005)CrossRefGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)zbMATHGoogle Scholar
  9. 9.
    Ekman, R.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (1997)Google Scholar
  10. 10.
    Ellgring, H.: Non-Verbal Communication in Depression. Cambridge University Press, Cambridge (2007)Google Scholar
  11. 11.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)zbMATHGoogle Scholar
  12. 12.
    Jan, A., Meng, H., Gaus, Y.F.A., Zhang, F., Turabzadeh, S.: Automatic depression scale prediction using facial expression dynamics and regression. In: Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge, pp. 73–80. ACM (2014)Google Scholar
  13. 13.
    Kroenke, K., Spitzer, R.L., Williams, J.B.: The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16(9), 606–613 (2001)CrossRefGoogle Scholar
  14. 14.
    Mehrabian, A., Russell, J.A.: An Approach to Environmental Psychology. The MIT Press, Cambridge (1974)Google Scholar
  15. 15.
    Meng, H., Huang, D., Wang, H., Yang, H., Ai-Shuraifi, M., Wang, Y.: Depression recognition based on dynamic facial and vocal expression features using partial least square regression. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, pp. 21–30. ACM (2013)Google Scholar
  16. 16.
    Nasir, M., Jati, A., Shivakumar, P.G., Nallan Chakravarthula, S., Georgiou, P.: Multimodal and multiresolution depression detection from speech and facial landmark features. In: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, pp. 43–50. ACM (2016)Google Scholar
  17. 17.
    Nutt, D.: The Hamilton depression scale- accelerator or break on antidepressant drug discovery? J. Neurol. Neurosurg. Psychiatry 85, 119–120 (2014).  https://doi.org/10.1136/jnnp-2013-306984CrossRefGoogle Scholar
  18. 18.
    Organization, W.H., et al.: Depression and other common mental disorders: global health estimates. Technical report, World Health Organization (2017)Google Scholar
  19. 19.
    Pampouchidou, A., et al.: Quantitative comparison of motion history image variants for video-based depression assessment. EURASIP J. Image Video Process. 2017(1), 64 (2017) CrossRefGoogle Scholar
  20. 20.
    Pampouchidou, A., et al.: Depression assessment by fusing high and low level features from audio, video, and text. In: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, pp. 27–34. ACM (2016)Google Scholar
  21. 21.
    Pampouchidou, A., et al.: Automatic assessment of depression based on visual cues: a systematic review. IEEE Trans. Affect. Comput. (2017).  https://doi.org/10.1109/TAFFC.2017.2724035
  22. 22.
    Pearson, K.: Notes on the history of correlation. Biometrika 13(1), 25–45 (1920)CrossRefGoogle Scholar
  23. 23.
    Ringeval, F., et al.: AVEC 2017: real-life depression, and affect recognition workshop and challenge. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 3–9. ACM (2017)Google Scholar
  24. 24.
    Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2014)CrossRefGoogle Scholar
  25. 25.
    Schumann, I., Schneider, A., Kantert, C., Löwe, B., Linde, K.: Physicians attitudes, diagnostic process and barriers regarding depression diagnosis in primary care: a systematic review of qualitative studies. Fam. Pract. 29(3), 255–263 (2011)CrossRefGoogle Scholar
  26. 26.
    Sun, B., et al.: A random forest regression method with selected-text feature for depression assessment. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 61–68. ACM (2017)Google Scholar
  27. 27.
    Valstar, M., et al.: AVEC 2016: depression, mood, and emotion recognition workshop and challenge. In: Proceedings of the 6th International Workshop on Audio/visual Emotion Challenge, pp. 3–10. ACM (2016)Google Scholar
  28. 28.
    Williamson, J.R., et al.: Detecting depression using vocal, facial and semantic communication cues. In: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, pp. 11–18. ACM (2016)Google Scholar
  29. 29.
    Williamson, J.R., Quatieri, T.F., Helfer, B.S., Horwitz, R., Yu, B., Mehta, D.D.: Vocal biomarkers of depression based on motor incoordination. In: Proceedings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge, pp. 41–48. ACM (2013)Google Scholar
  30. 30.
    Yang, L., Jiang, D., Xia, X., Pei, E., Oveneke, M.C., Sahli, H.: Multimodal measurement of depression using deep learning models. In: Proceedings of the 7th Annual Workshop on Audio/Visual Emotion Challenge, pp. 53–59. ACM (2017)Google Scholar
  31. 31.
    Zhu, Y., Shang, Y., Shao, Z., Guo, G.: Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans. Affect. Comput. 9(4), 578–584 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJNUDelhiIndia
  2. 2.Hansraj CollegeUniversity of DelhiDelhiIndia

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