A Convolutional Neural Network Study on Depression and Eye Blink Analysis
Bryan G. Dadiz1, Alfio I. Regla2
1Bryan G. Dadiz, College of Computer Studies, Technological Institute of the Philippines, Manila, Philippines.
2Alfio I. Regla, College of Computer Studies, Technological Institute of the Philippines, Manila, Philippines.
Manuscript received on 04 March 2023 | Revised Manuscript received on 16 March 2023 | Manuscript Accepted on 15 April 2023 | Manuscript published on 30 April 2023 | PP: 7-11 | Volume-12 Issue-5, April 2023 | Retrieval Number: 100.1/ijitee.E94880412523 | DOI: 10.35940/ijitee.E9488.0412523
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study is determining the correlation of human blinks relating to depression. The study uses convolutional neural network for detecting blinks in a video. Using Closed Eyes in the Wild dataset the Convolution Neural Network model was trained having 99.24% in training accuracy and 0.0275 loss from epoch of 50. However, the results from validation of the model resulted 61.09% tested from two datasets that where labelled with BDI-II depression scale. The study collated the results of recorded blinks from the video datasets and it showed that there is a weak positive correlation of the recorded blinks computed as blinking rates to depression. The result showed that the r2 score was 0>3.4 thus, there is a possibility but not the highly indicator of depression.
Keywords: AVEC’ 14 Introduction, Convolution Neural Network, Depression Analysis, Eye Blinks, Major Depressive Disorder, Non-Verbal Behavior, Visual Cue
Scope of the Article: Visual Analytics