TY - JOUR AU - Dharamshi, Ameer AU - Zou, Rosie Yuyan PY - 2019/07/17 Y2 - 2024/03/29 TI - CSEye: A Proposed Solution for Accurate and Accessible One-to-Many Face Verification JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.33019933 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5103 SP - 9933-9934 AB - <p>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.</p> ER -