Image-Based Stress Prediction from Visual Cues Using Deep Learning Models

Stress is a critical factor affecting human health and performance, and early detection
through non-invasive methods is increasingly vital in modern healthcare and workplace
environments. This study presents a deep learning-based approach for predicting stress levels
from facial images using Wavelet Transform architectures.Detecting stress through visual
cues has become an increasingly important area of research in affective computing and
mental health assessment. This study proposes a deep learning-based framework for stress
detection from facial images by leveraging subtle visual cues such as facial expressions,
muscle tension, and micro-expressions. We evaluate severaldeep learning models—including
Wavelet Transformtoanalyze their effectiveness in capturing both spatial and temporal
patterns associated with stress. A publicly available dataset of labeled facial images is used to
train and validate the models, with preprocessing steps including face detection,
normalization, and data augmentation. Experimental results demonstrate that CNN-based
models can effectively learn stress-related features from facial imagery, achieving high
classification accuracy and strong generalization across individuals. This work underscores
the potential of visual deep learning models as a non-invasive, real-time solution for
automated stress monitoring in health, education, and workplace applications