Manuscript Title:

STRESS DETECTOR: A DEEP LEARNING BASED EMOTION RECOGNITION AND LANDMARK DETECTION SYSTEM

Author:

ZAEEM NAZIR, SADIA TARIQ, TALHA MAJEED, SAIRA TARIQ, FARHANA TABASSUM, USAMA NAWAZ

DOI Number:

DOI:10.5281/zenodo.12703997

Published : 2024-07-10

About the author(s)

1. ZAEEM NAZIR - Department of CS & IT, University of Narowal, Pakistan. 2. SADIA TARIQ - Department of Computer Science and IT, The University of Lahore, Pakistan. 3. TALHA MAJEED - Department of Computer Science, University of Engineering & Technology Lahore, Pakistan 4. SAIRA TARIQ - Department of CS & IT, University of Narowal, Pakistan. 5. FARHANA TABASSUM - Department of CS & IT, University of Narowal, Pakistan. 6. USAMA NAWAZ - Department of CS & IT, University of Narowal, Pakistan.

Full Text : PDF

Abstract

Stress, a prevalent issue in today's fast-paced world, has significant implications on physical and mental health. Recognizing the global impact of stress-related diseases, we embarked on a journey to develop an intelligent stress detection system. Our approach integrates facial expression analysis, landmark detection, and deep learning to provide real-time assessment of stress levels. Leveraging a dataset of facial images and deep learning models, we customized a Convolutional Neural Network (CNN) as an optimal choice for emotion recognition, achieving remarkable results. Incorporating physiological indicators, we devised a stress calculation formula that categorizes stress into four states. Our user-friendly web interface empowers users to monitor their stress trends overtime. This research showcases the effectiveness of learning based assistive technologies for stress management and significant improvement in accuracy of classifier models.


Keywords

Emotion Recognition, Lips and Eyebrow Landmarks, Stress Calculation, Stress States.