RESEARCH ON THE EFFICIENCY OF COMBINED EMBEDDINGS FOR FACIAL VERIFICATION
DOI:
https://doi.org/10.18372/2410-7840.26.18831Keywords:
Facial Verification, Biometric Authentication, Neural Networks, Concatenated EmbeddingsAbstract
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.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).