Multi-directional local gradient descriptor: A new feature descriptor for face recognition

The performance of the face recognition systems is vulnerable to occlusion, light and expression changes and such constraints need to be handled effectively in a robust face recognition system. This paper presents a new multi -directional local gradient descriptor (MLGD) method for face recognition based on local directional gradient features that exploit the edges/line information in multiple directions. The proposed technique exploits advantage of similarity of a face image in small blocks. The weighted gradient features of face images in different directions and zones are computed based on co -relation between pixel elements. These features referred to as multi -directional local gradient descriptor (MLGD), which capture adequate edge information by integrating different directional gradients. Further, the directional gradient features extracted through MLGD operator are represented as a symbolic data object. The face identification is carried out by using the symbolic object representation of test image and employing a symbolic similarity measure. The experimental results on AR (97.33%) and LFW (97.25%) benchmark face databases demonstrate that the symbolic data representation of the new directional gradient magnitude of face image significantly improves the recognition performance as compared to local gradient descriptors and other state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.