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.
@article{arxiv.1708.09317,
title = {Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network},
author = {Amarjot Singh and Devendra Patil and G Meghana Reddy and SN Omkar},
journal= {arXiv preprint arXiv:1708.09317},
year = {2017}
}
Comments
To Appear in the IEEE International Conference on Computer Vision Workshops (ICCVW) 2017