Grey Wolf Optimization for Face Recognition

Abstract

Facial recognition technology, known for its precision and user-friendly interface, plays a pivotal role in security solutions. This study, entitled "Enhancing Face Recognition through Grey Wolf Optimization" focuses on the accurate identification of faces using trained images. Employing advanced machine learning techniques, specifically grey wolf optimization and enhanced capsule networks, the project makes significant strides in improving vector capture and classification accuracy, thereby elevating overall performance. The integration of these innovative techniques ensures the provision of highly precise values for vector classification and face recognition. With a specific emphasis on sectors such as Law Enforcement, Marketing, Advertisements, and Education, the research facilitates swift and accurate face recognition within a condensed timeframe, resulting in enhanced overall performance across these industries. By synergizing the capabilities of grey wolf optimization and enhanced capsule networks, this research project emerges as a valuable asset addressing the diverse needs of sectors requiring precise and efficient facial recognition capabilities. The study introduces a novel approach to optimizing face recognition technology, promising substantial benefits for security and various industry applications. In summary, the strategic combination of machine learning methodologies in this research contributes to the advancement of facial recognition technology, presenting a robust solution tailored to the specific demands of contemporary sectors and industries.