A covariate of face recognition is defined as an effect that independently increases the intra-class variability or decreases the inter-class variability or both. Covariates such as pose, illumination, expression, aging, and disguise are established and extensively studied in literature and are categorized as existing covariates of face recognition. However, ever increasing applications of face recognition have instigated many new and exciting scenarios such as matching forensic sketches to mug-shot photos, faces altered due to plastics surgery, low resolution surveillance images, and individual from videos. These covariates are categorized as emerging covariates of face recognition which is the primary emphasis of this dissertation. In this talk, I will be focussing on the emerging covariates of face recognition. First, we present an automated algorithm to extract discriminative information from local regions of both sketches and digital images using MCWLD. An evolutionary memetic optimization is proposed to assign optimal weights to every local facial region to boost the identification performance. Secondly, we present a multi-objective evolutionary granular algorithm for matching face images altered due to plastic surgery procedures. Thirdly, we present a co-transfer learning framework which is a cross pollination of transfer learning and co-training paradigms, for enhancing the performance of cross-resolution face recognition. Finally, the fourth contribution of this dissertation is a video based face recognition algorithm which computes a discriminative video signature as an ordered (ranked) list of still face images from a large dictionary. This talk will present analysis of different algorithms and comprehensive experimental evaluation on different publicly available datasets.