Visual Depression Diagnosis From Face Based on Various Classification Algorithms

Nasser, Sana A.; Hashim, Ivan A.; Ali, Wisam H.

doi:10.30684/etj.v38i11A.1714

	Visual Depression Diagnosis From Face Based on Various Classification Algorithms
Engineering and Technology Journal
Article 14, Volume 38, 11A, 2020, Pages 1717-1729 PDF (1.17 M)
Document Type: Research Paper
DOI: 10.30684/etj.v38i11A.1714
Authors
Sana A. Nasser^* ¹; Ivan A. Hashim²; Wisam H. Ali³
¹University of Technology, Department of Electrical Engineering, Baghdad, Iraq,
²University of Technology-Iraq
³Department of Electrical Engineering University of Technology. Baghdad. Iraq
Abstract
Most 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.
Keywords
Visual depression diagnosis; Depression detection; Facial action coding system; KNN; SVM; PCA; LDA

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