Image Based Stress Detection Using Deep Learning

Project Code :TCMAPY1066


The objective of this project is to develop and evaluate a deep learning model, combining Convolutional Neural Networks (CNN) and MobileNet, to accurately detect and quantify stress in individuals through image analysis, with the aim of providing timely interventions and support for mental well-being.


Stress is a prevalent and detrimental aspect of modern life, impacting mental and physical well-being. The ability to detect and manage stress is of paramount importance. This study proposes a novel approach for stress detection through image analysis using Convolutional Neural Networks (CNN), particularly the efficient Mobile Net architecture. The core idea is to leverage facial expressions as indicators of stress levels, as they often exhibit distinctive patterns during stressful situations. An extensive dataset of facial images encompassing various stress levels is collected and preprocessed. Mobile Net, known for its efficiency and effectiveness in image classification tasks, is employed as the backbone architecture for feature extraction. The CNN model is fine-tuned using transfer learning techniques to adapt to the stress detection task. During training, the network learns to recognize subtle facial cues associated with stress, allowing it to classify input images into different stress levels. The proposed system demonstrates promising results in real-time stress detection, offering a non-intrusive and accessible means for assessing stress in individuals. This innovative application of deep learning and Mobile Net architecture has the potential to provide valuable insights into stress management and mental health monitoring, ultimately improving the well-being of individuals in our fast-paced, stress-prone society.

Keywords: CNN, Mobile Net, Deep Learning, stress detection, transfer learning 

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

Block Diagram


H/W Specifications:

Processor                 :  I5/Intel Processor

RAM                             :  8GB (min)

Hard Disk                   :  128 GB

S/W Specifications:

Operating System            :   Windows 10

Server-side Script             :   Python 3.6

IDE                     :   PyCharm,Jupyter notebook

Libraries Used     :   Numpy, IO, OS, Flask, keras, pandas, tensorflow

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