Enhancing Discriminative Power in Facial Recognition Systems through Crucial Facial Region Analysis

Abstract

For a variety of reasons, including differences in illumination, position, and occlusions, facial recognition systems are becoming increasingly ubiquitous in a variety of applications; nonetheless, they frequently fail to achieve high discriminative power. This article presents a unique way to improve the discriminative power of face recognition systems by analysing critical facial areas, notably the eyes, nose, and mouth. The purpose of this approach is to raise the sensitivity of facial recognition systems. We hope that by concentrating on these two critical areas, we will be able to enhance feature extraction and representation, which will ultimately result in recognition that is more accurate and dependable. For the purpose of properly isolating and analysing these key regions, our methodology incorporates complex machine learning and deep learning techniques, as well as attention processes. Enhanced capability to discriminate between individuals is achieved by the utilization of a hybrid feature extraction approach that combines geometric and appearance-based information.

Our methodology has been shown to be effective through the use of experimental assessments on benchmark datasets such as FER2013, CK+, and JAFFE. With regard to identification accuracy, precision, recall, and F1 score, the suggested system makes considerable improvements in comparison to the systems that are currently in use. According to these findings, facial area analysis could be able to significantly improve the performance of face recognition systems, hence making them more reliable and adaptable to situations that occur in the real world. When it comes to boosting the overall discriminative capacity of recognition systems, our work not only contributes to the advancement of the area of facial recognition but also brings to light the significance of focused feature analysis. In subsequent study, the integration of this method with real-time video processing will be investigated, along with the implications that this integration has for applications in the fields of healthcare and security.