Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and proposed ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods has been introduced to overcome these critical issues to improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern are proposed by extending an existing descriptor - local directional pattern (LDP). Further, the two feature vectors obtained by these methods are concatenated to form a hybrid descriptor. Experimentation has been carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis. © 2020, Electronic Letters on Computer Vision and Image Analysis. All Rights Reserved