DLGBD: A directional local gradient based descriptor for face recognition

Abstract

This paper proposes a novel high-performance gradient-based local descriptor that handles the prominent challenges of face recognition such as resistance against rotational, illuminative changes as well as noise effects. One of the novelties this study poses is that, while processing the gradient for each direction, an analysis is done by considering the predecessors of the corresponding pixel as well as the successors at that direction. Furthermore, earlier studies represent these local relationships by encoding them in binary because they consider only the positive and negative intensity changes. However, we propose an alternative way of representation that encodes the relationships between each pixel and its neighbors in a multi-valued logic manner called Directional Local Gradient Based Descriptor (DLGBD). Our method not only considers the variations but also uniformity. A threshold value is defined to identify whether an intensity variation is present in the specified direction. If the intensity change exceeds the threshold value, then it is evaluated as a variation either in positively or negatively depending on the direction of the change. Three states of the relationship between multiple pixels at each direction yield a more discriminative descriptor for face retrieval. Ternary logic is applied to express three states. Ternary values that are calculated at each direction are concatenated and the resulting compound ternary value is replaced with the reference pixel. By this way, a more discriminative face descriptor is achieved which is resistant to noise and challenges in unconstrained environments. Extensive simulations are conducted over benchmark datasets and the performance of DLGBD is compared to the other state-of-the-art methods. As presented by the simulation results, the DLGBD achieves very high discriminating performance as well as providing resistance against rotation and illumination variations.