Visual Depression Diagnosis From Face Based on Various Classification Algorithms
Engineering and Technology Journal,
2020, Volume 38, Issue 11, Pages 1717-1729
AbstractMost psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.
 “Depression.” [Online]. Available: https://www.who.int/health-topics/depression#tab=tab_1. [Accessed: 01-Feb-2020].
 T. P. Blackburn, “Depressive disorders: Treatment failures and poor prognosis over the last 50 years,” Pharmacol. Res. Perspect., vol. 7, no. 3, pp. 1–20, 2019.
 S. Al-Gawwam and M. Benaissa, “Depression Detection from Eye Blink Features,” in 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT, 2018, pp. 388–392.
 J. F. Cohn et al., “Detecting depression from facial actions and vocal prosody,” in Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, 2009, pp. 1–7.
 P. VIOLA and M. J. JONES, “Robust Real-Time Face Detection,” Int. J. Eng. Technol., vol. 7, no. 2, pp. 29–32, 2004.
 T. Baltrušaitis, P. Robinson, and L.-P. Morency, “Constrained Local Neural Fields for robust facial landmark detection in the wild,” in In Proceedings of the IEEE international conference on computer vision workshops, 2013, pp. 354–361.
 E. S. Mikhailova, T. V. Vladimirova, A. F. Iznak, E. J. Tsusulkovskaya, and N. V. Sushko, “Abnormal recognition of facial expression of emotions in depressed patients with major depression disorder and schizotypal personality disorder,” Biol. Psychiatry, vol. 40, no. 8, pp. 697–705, Oct. 1996.
 Q. Wang, H. Yang, and Y. Yu, “Facial expression video analysis for depression detection in Chinese patients,” J. Vis. Commun. Image Represent. vol. 57, no. November, pp. 228–233, 2018.
 T. Baltrušaitis, M. Mahmoud, and P. Robinson, “Cross-dataset learning and person-specific normalization for automatic Action Unit detection,” in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG, 2015, vol. 2015-Janua, pp. 1–6.
 İ. Babaoğlu, “Diagnosis of Coronary Artery Disease Using Artificial Bee Colony and K-Nearest Neighbor Algorithms,” Int. J. Comput. Commun. Eng., vol. 2, no. 1, pp. 56–59, 2013.
 I. Nurwauziyah, U. D. S, I. G. B. Putra, and M. I. Firdaus, “Satellite Image Classification using Decision Tree , SVM and k-Nearest Neighbor,” no. July, 2018.
 B. Schölkopf, “SVMs - A practical consequence of learning theory,” IEEE Intell. Syst. Their Appl., vol. 13, no. 4, pp. 18–21, Jul. 1998.
 A. Hulaj, A. Shehu, and X. Bajrami, “Support Vector Machine for the Classification of Images Captured by WMSN,” in Proceedings - 2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO, 2017, pp. 283–287.
 Q. Wang, Q. Gao, X. Gao, and F. Nie, “Angle principal component analysis,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 7, no. 5, pp. 2936–2942, 2017.
 B. J. Frey, “Pattern Classification,” in Graphical Models for Machine Learning and Digital Communication, 2018, p. 654.
 D. Acquisition et al., “Naval Postgraduate,” vol. 298, no. June, pp. 405–405, 2010.
 K. Torkkola, “Linear Discriminant Analysis in Document Classification,” in International Conference Data Mining Workshop on Text Mining, 2001, no. October, pp. 1–10.
 T. Qin, T. Y. Liu, X. D. Zhang, D. S. Wang, and H. Li, “Global ranking using Continuous Conditional Random Fields,” in Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, 2009, pp. 1281–1288.
 G. Jackson-Koku, “Beck depression inventory,” Occup. Med. (Chic. Ill)., vol. 66, no. 2, pp. 174–175, 2016.
 N. C. Maddage, R. Senaratne, L. S. A. Low, M. Lech, and N. Allen, “Video-based detection of the clinical depression in adolescents,” in in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC, 2015, pp. 3723–3726.
 G. Stratou, S. Scherer, J. Gratch, and L. Morency, “Automatic nonverbal behavior indicators of depression and PTSD : the effect of gender,” J. Multimodal User Interfaces, vol. 9, 2014.
 M. Senoussaoui, M. Sarria-paja, J. F. Santos, and T. H. Falk, “Model Fusion for Multimodal Depression Classification and Level Detection,” Proc. 4th Int. Work. Audio/Visual Emot. Challenge, 2014, pp. 57-63.
 S. Alghowinem, R. Goecke, J. F. Cohn, M. Wagner, G. Parker, and M. Breakspear, “Cross-cultural detection of depression from nonverbal behavior,” in 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG ,2015, pp. 1-8.
 T. H. Yang, C. H. Wu, K. Y. Huang, and M. H. Su, “Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio–visual signals,” J. Ambient Intell. Humaniz. Comput., vol. 8, no. 6, pp. 895–906, 2017.
 S. Harati, A. Crowell, H. Mayberg, J. Kong, and S. Nemati, “Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS,2016, pp. 2254–2257.
 J. M. Girard, J. F. Cohn, M. H. Mahoor, S. Mavadati, and D. P. Rosenwald, “Social risk and depression: Evidence from manual and automatic facial expression analysis,” 2013 10th IEEE Int. Conf. Work. Autom. Face Gesture Recognition, FG, pp. 1–8, and 2013.
 H. Dibeklioglu, Z. Hammal, and J. F. Cohn, “Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding,” IEEE J. Biomed. Heal. Informatics, vol. 22, no. 2, pp. 525–536, 2018.
 A. Jan, H. Meng, Y. F. A. Gaus, F. Zhang, and S. Turabzadeh, “Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression Categories and Subject Descriptors,” Proc. 4th Int. Work. Audio/Visual Emot. Chall., pp. 73–80, 2014.
 D. Venkataraman, “Extraction of Facial Features for Depression Detection among Students,” Int. J. Pure Appl. Math., vol. 118, no. 7, pp. 455–463, 2018.
 B. G. Dadiz, C. R. Ruiz, T. Manila, and Q. Manila, “Detecting Depression in Videos using Uniformed Local Binary Pattern on Facial Features,” pp. 413–422, 2019.
 J. M. Twenge, A. B. Cooper, T. E. Joiner, M. E. Duffy, and S. G. Binau, “Age, Period, and Cohort Trends in Mood Disorder Indicators and Suicide-Related Outcomes in a Nationally Representative Dataset, 2005-2017,” J. Abnorm. Psychol., vol. 128, no. 3, pp. 185–199, Apr. 2019.
 “Understand the News.” [Online]. Available: https://www.vox.com/. [Accessed: 09-Feb-2
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