Abstract
Facial verification is a core problem studied by researchers in computer vision. Recently published one-to-one comparison models have successfully achieved accuracy results that surpass the abilities of humans. A natural extension to the one-to-one facial verification problem is a one-to-many classification. In this abstract, we present our exploration of different methods of performing one-to-many facial verification using low-resolution images. The CSEye model introduces a direct comparison between the features extracted from each of the candidate images and the suspect before performing the classification task. Initial experiments using 10-to-1 comparisons of faces from the Labelled Faces of the Wild dataset yield promising results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the AAAI Conference on Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
