Vision Transformer-Based Systems Achieve State- Of-The-Art In Stress Prediction From Facial Images

Stress is the body’s natural reaction to demands,
which can cause tension in the body or mind. It is positive by
helping people stay alert and motivated, but prolonged stress
lead to negative health effects. The first step in the pipeline i s
input data acquisition, where images often facial or biomedical
in nature are gathered for analysis. These images undergo an
initial preprocessing phase using CLAHE. This enhancement
technique is both fast and lightweight, ensuring efficient
processing while significantly improving local contrast, thereby
making subtle differences in image regions more apparent.
Next, the preprocessed images are sent through a segmentation
module that utilizes a Wavelet Transform. This approach
excels at retaining important image boundaries (edge
preservation) and is robust to noise, yielding cleaner and more
usable image segments for downstream analysis. The
segmented images then undergo a feature extraction stage,
utilizing Local Binary Patterns (LBP). LBP quickly
summarizes local structures within the images, contributing
both computational efficiency and the ability to discriminate
subtle texture variations, an essential aspect in the accurate
detection of stress indicators in visual data. Extracted features
are subsequently fed into a classification module powered by a
Vision Transformer. This modern architecture provides both
high accuracy in classification tasks and interpret-ability,
allowing the reason behind predictions to be better understood
and trusted. The entire classification system is deployed using a
Flask- based web application, integrating with web cameras
for real-time data collection. This enables users to interact with
the system and receive immediate feedback on their stress
status. Finally, the user-facing interface makes the prediction
output the outcome of this reliable pipeline is available,
providing users with an evaluation of their stress level based on
the analyzed images. This detailed process ensures accuracy,
speed, and usability from gathering data to producing
actionable prediction outcome