ENHANCED VGG-BASED APPROACH FOR FACIAL EXPRESSION-DRIVEN STRESS DETECTION

Abstract

Stress is one of the major contributing factors to many diseases in today’s lifestyle. Although it is not feasible to completely eradicate stress from our daily lives, we can reduce it to a certain level. This paper presents a cutting-edge method for stress detection that utilizes an advanced version of the VGG (Visual Geometry Group) architecture by analysing facial features obtained from images. Our objective is to accurately detect stress indicators, enabling prompt intervention and assistance. The enhanced VGG architecture significantly improves the model’s capacity to identify subtle changes in facial expressions linked to mental stress. The performance of detection is further optimized through comprehensive training and validation on a varied dataset. This paper contributes to the evolution of stress detection techniques using modified VGG with Inception layers (VGGIL) and provides significant applications in healthcare, mental wellness, and human-computer interaction by harnessing the power of deep learning and facial recognition technology.