2017/08/30 by Amarjot Singh, Singh, Amarjot, Devendra Patil +5 · 1 voice
Computer Science · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1708.09317
To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 2017
arxiv created 2017/08/30 · openalex publication_date 2017/08/30 · arxiv published 2017/08/30 · arxiv updated 2017/08/31 · openalex created_date 2022/09/25 · openalex updated_date 2026/07/28
Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods.