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 G . . .LCM, 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
Face recognition remains critical and up-to-date due to its undeniable contribution to security. Many descriptors, the most vital figures used for face discrimination, have been proposed and continue to be done. This article presents a novel and highly discriminative identifier that can maintain high recognition performance, even under high noise, varying illumination, and expression exposure. By evolving the image into a graph, the feature set is extracted from the resulting graph rather than making inferences directly on the image pixels as done conventionally. The adjacency matrix is created at the outset by considering the pixel . . .s’ adjacencies and their intensity values. Subsequently, the weighteddirected graph having vertices and edges denoting the pixels and adjacencies between them is formed. Moreover, the weights of the edges state the intensity differences between the adjacent pixels. Ultimately, information extraction is performed, which indicates the importance of each vertex in the graphic, expresses the importance of the pixels in the entire image, and forms the feature set of the face image. As evidenced by the extensive simulations performed, the proposed graphic-based identifier shows remarkable and competitive performance regarding recognition accuracy, even under extreme conditions such as high noise, variable expression, and illumination compared with the state-of-the-art face recognition methods
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