This paper presents a multi-pose face recognition approach using hybrid face features descriptors (HFFD). The HFFD is a face descriptor containing of rich discriminant information that is created by fusing some frequency-based features extracted using both wavelet and DCT analysis of several different poses of 2D face images. The main aim of this method is to represent the multi-pose face images using a dominant frequency component with still having reasonable achievement compared to the recent multi-pose face recognition methods. The HFFD based face recognition tends to achieve better performance than that of the recent 2D-based face recognition method. In addition, the HFFD-based face recognition also is sufficiently to handle large face variability due to face pose variations .
@article{arxiv.1703.04062,
title = {Multi-Pose Face Recognition Using Hybrid Face Features Descriptor},
author = {I Gede Pasek Suta Wijaya and Keiichi Uchimura and Gou Koutaki},
journal= {arXiv preprint arXiv:1703.04062},
year = {2017}
}