Video Based Deep CNN Model for Depression Detection
Article Sidebar
Main Article Content
Abstract
Our face reflects our feelings towards anything and everything we see, smell, teste or feel through any of our senses. Hence multiple attempts have been made since last few decades towards understanding the facial expressions. Emotion detection has numerous applications since Safe Driving, Health Monitoring Systems, Marketing and Advertising etc. We propose an Automatic Depression Detection (ADD) system based on Facial Expression Recognition (FER).
We propose a model to optimize the FER system for understanding seven basic emotions (joy, sadness, fear, anger, surprise, disgust and neutral) and use it for detection of Depression Level in the subject. The proposed model will detect if a person is in depression and if so, up to what extent. Our model will be based on a Deep Convolution Neural Network (DCNN).
Article Details
References
Ekaterina Ivanovaa and Georgii Borzunov: "10th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2019 (Tenth Annual Meeting of the BICA Society)" (Page No. 244–248) – 2019: Optimization of machine learning algorithm of emotion recognition
Mihai Gavrilescu * and Nicolae Vizireanu: Sensors 2019, 19, 3693; doi:10.3390/s19173693, 2019 "Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System"
Zeyu Pan, Huimin Ma, Lin Zhang, Yahui Wang IEEE ICIP 2019, 2019 DEPRESSION DETECTION BASED ON REACTION TIME AND EYE MOVEMENT
Shalini Bhatia, Roland Goecke, Zakia Hammal, Jeffrey F Cohn Proc Int Conf Autom Face Gesture Recognit. 2019 May ; 2019: . doi:10.1109/FG.2019.8756509. 2019 Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews
Gudni Johannesson, & Nazzal Salem. (2022). Design Structure of Compound Semiconductor Devices and Its Applications. Acta Energetica, (02), 28–35. Retrieved from http://actaenergetica.org/index.php/journal/article/view/466
Fabien Ringeval, Björn Schuller, Michel Valstar, Nicholas Cummins, Roddy Cowie, Leili Tavab, Maximilian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner, Siyang Song, "Association for Computing Machinery. ACM ISBN 978-1-4503-5983-2/18/10.", 2019 "Audio/Visual Emotion Challenge 2019: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition"
Wheidima Carneiro de Melo1, Eric Granger2 and Abdenour Hadid "978-1-7281-0089-0/19/$31.00 c 2019 IEEE", 2019 "Combining Global and Local Convolutional 3D Networks for Detecting Depression from Facial Expressions"
Mohamad Al Jazaery and Guodong Guo IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2020 "Video-Based Depression Level Analysis by Encoding Deep Spatiotemporal Features"
Rolf Bracke, & Nouby M. Ghazaly. (2022). An Exploratory Study of Sharing Research Energy Resource Data and Intellectual Property Law in Electrical Patients. Acta Energetica, (01), 01–07. Retrieved from http://actaenergetica.org/index.php/journal/article/view/459
Mingyue Niu, Jianhua Tao, Bin Liu 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), 2019 Local Second-Order Gradient Cross Pattern for Automatic Depression Detection
Aven Samareh, Yan Jin, Zhangyang Wang, Xiangyu Chang & Shuai Huang IISE Transactions on Healthcare Systems Engineering , 2018 "Detect Depression from Communication: How Computer Vision, Signal Processing, and Sentiment Analysis Join Forces"
Jian Zhao1 • Weiwen Su1 • Jian Jia2 • Chao Zhang1 • Tingting Lu1 Springer Science+Business Media, LLC, part of Springer Nature 2017, 2017 "Research on depression detection algorithm combine acoustic rhythm with sparse face recognition"
"Stefan Scherer a,?, Giota Stratou a, Gale Lucas a, Marwa Mahmoud a,b, Jill Boberg a, Jonathan Gratch a, Albert (Skip) Rizzo a, Louis-Philippe Morency" Image and Vision Computing 32 (2014) 648–658, 2014 "Automatic audiovisual behavior descriptors for psychological disorder analysis"
James R. Williamson, Thomas F. Quatieri, Brian S. Helfer, Rachelle Horwitz, Bea Yu, Daryush D. Mehta, 2013 "Vocal Biomarkers of Depression Based on Motor Incoordination"
Prudhvi Raj Dachapally , 2019 Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
Zhenhai Liu, Hanzi Wang(?), Yan Yan, and Guanjun Guo, 2015 "Effective Facial Expression Recognition via the Boosted Convolutional Neural Network"
PAUL VIOLA, MICHAEL J. JONES, 2003 Robust Real-Time Face Detection
P. Ekman, “Darwin, deception, and facial expression,” Annals of the New York Academy of Sciences, vol. 1000, no. 1, pp. 205–221, 2003.
https://www.who.int/news-room/fact-sheets/detail/depression
https://apps.who.int/iris/bitstream/handle/10665/131056/9789241564779_eng.pdf
https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(20)30482-X/fulltext
https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(20)30491-0/fulltext