Designing and Developing a Facial Login System Using MATLAB and ORB

Face recognition, as a prominent biometric modality, has witnessed significant growth over the past two decades, becoming pivotal in various commercial and security applications, particularly in identity validation and recognition (Turk & Pentland, 1991) [29]. The evolution of reliable security systems, coupled with advancements in image processing and pattern recognition, has spurred research into new methodologies for enhancing face recognition systems. This thesis focuses on designing and developing a facial login system utilizing MATLAB and ORB (Oriented FAST and Rotated BRIEF) algorithms to provide a secure and efficient authentication mechanism (Rublee et al., 2011) [40].

This thesis aims to design and implement a robust automated face recognition system capable of handling diverse environmental conditions, such as varying noise levels, illumination changes, and occlusions. The ORB algorithm is leveraged for its efficiency in feature detection and description, offering computational benefits over traditional methods (Lowe, 2004). A novel method based on the illumination-reflectance model is proposed for illumination-invariant feature extraction. This approach is computationally efficient and does not rely on prior knowledge of face models or illumination conditions. Additionally, a weighted voting scheme leveraging mutual information and entropy is introduced to improve performance under illumination variations and mitigate occlusions (Viola & Jones, 2001) [30]. This ensemble classifier approach effectively minimizes the impact of environmental factors on face image quality. Through experimental validation, the proposed system demonstrates significant advancements in overcoming challenges commonly faced by existing face recognition systems. By achieving robustness against illumination changes and occlusions, it contributes to enhancing the reliability and applicability of facial biometrics in real-world scenarios.