Abstract:
Many face-recognition (FR) methods have been proposed thus far. Although FR has achieved
wisdom in square pixel-based image processing (SIP) due to many studies, this wisdom has not been
transferred to Hexagonal pixel-based image processing (HIP) until now. This study presents HIP versions of
the most basic texture extraction studies in SIP, namely Gray-Level-Co-occurrence-Matrices (GLCM), Local
Binary Pattern (LBP), and our recent work, local-holistic graph-based descriptor (LHGPD). The images
are first transformed from the SIP domain to the HIP domain. The HIP domain equivalences (HexGLCM,
HexLBP, and HexLHGPD) of the SIP domain GLCM, LBP, and LHGPD are then established. Finally, the
facial recognition performances of the SIP and HIP domain versions of GLCM, LBP, and LHGPD are
evaluated and compared on the primary data sets. The results of the experiments reveal that HIP domain
GLCM, LBP, and LHGPD show a par performance, surpassing them in places when compared to their
counterparts in the SIP domain regarding face recognition accuracy.
