RESEARCH ON THE EFFICIENCY OF COMBINED EMBEDDINGS FOR FACIAL VERIFICATION

Abstract

In the era of digital authentication, facial verification systems have become a cornerstone of security protocols across various applications. This study explores the performance synergy from concatenated embeddings in enhancing biometric authentication accuracy. By leveraging the Celebrities in Frontal-Profile dataset (CFP), we investigate whether the fusion of embeddings generated by models such as VGG-Face, Facenet, OpenFace, ArcFace, and SFace can result in a more robust authentication process. The approach involves computing the L2 distance between normalized concatenated embeddings of an input face image and an anchor, thereby determining the authenticity of the individual. Experiments are designed to compare the performance of singular model embeddings against concatenated embeddings, employing metrics such as accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). The findings of this research could significantly contribute to the development of more secure and reliable facial verification systems by using multiple existing models without the need for new model research, designing, and training.