A plethora of promising detectors and descriptors are available in Computer Vision for carrying out Face Recognition (FR) and although these techniques that form the backbone of FR have yielded reasonable efficacy, they are yet to advance to those levels where they demonstrate robust performance in unconstrained scenarios. In our deliberations, we employ the popular SIFT and SURF algorithms that are ubiquitously implemented in FR due to their remarkable potency in handling a variety of FR tasks. In this paper, we proffer two novel detector-descriptor variants to augment the proficiency of contemporary FR systems: (1) SURF detector with SIFT descriptor and (2) SIFT detector with SURF descriptor. We demonstrate the proficiency of the proposed techniques by utilizing pertinent mathematical arguments and performing comprehensive comparisons with the classical SIFT and SURF algorithms by employing a number of standard metrics over the benchmark LFW and Face94 datasets.