Stress Detection using Facial Image Data

Project Code :TCMAPY998


The objective of this project is to develop an efficient stress detection system using Convolutional Neural Networks (CNNs) and facial image data. By analyzing facial expressions and patterns, the CNN model aims to accurately identify signs of stress in individuals. This technology has potential applications in various fields, including mental health assessment, human-computer interaction, and stress management. The project seeks to enhance the accuracy, speed, and scalability of stress detection, contributing to a non-intrusive and automated approach for monitoring stress levels. Ultimately, the goal is to provide valuable insights into individuals' emotional well-being through real-time analysis of their facial images, aiding in timely interventions and support for stress-related issues.


The technological advancement and significant rise in the usage of social media has resulted in major psychological health problems such as stress, anxiety etc. These challenges can be analyzed and prevention strategies can be formulated. To overcome these severe problems, the urgent need is to monitor the blogs in social media as it is irrepressible by humans due to their strong desire towards SMEs (Social Media Environments). Traditional methods such as questionnaires and interviews were conducted by psychologists but these processes are time-consuming and hysteretic. In this paper we have surveyed various stress detection strategies and found to be ineffective to detect stress from social media. In this paper we proposed an Effective Stress Detection method to utilize the ontology for stress detection among individuals and taking necessary precautions to prevent the users from committing suicide. Ontology is the keyword matching search process used in social media to identify the stress-related messages shared among individuals with improved accuracy.

KEYWORDS: SME, Stress, detection, psychologists.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram




RAM: 8GB, 64 bit os. 

Processor: I3/Intel processor

Hard Disk Capacity: 128 GB +


Technology: Python, Application.

Libraries: Pandas, Numpy, Tensorflow, OS.

Version: Python 3.6+

Server side scripts: HTML, CSS, JS

Frame works: Flask

IDE: Pycharm 

Database: MySQL

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