A local-holistic graph-based descriptor for facial recognition

Abstract

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 pixels’ adjacencies and their intensity values. Subsequently, the weighted-directed 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. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.