Face recognition techniques & challenges

Abstract

Facial recognition technology has quickly become a critical component of today's security, authentication, and social networking systems. This study presents various face recognition techniques and discusses the challenges that arise in their application.

The study begins with a comprehensive literature review that details the development from traditional feature-based methods such as Eigenfaces and LDA to modern deep learning approaches such as convolutional neural networks (CNNs).

Through qualitative case study analysis, this thesis explores the application of facial recognition technology in various domains including airport security, smartphone authentication, and social media platforms. The analysis highlights practical benefits such as increased security and user convenience, but also identifies significant challenges such as variations in lighting, pose and facial expression, and ethical issues.

The findings suggest that while facial recognition technology has made significant progress, further research is needed to improve algorithm robustness, address ethical concerns, and ensure fair and unbiased system performance.