The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of large quantities of digital data. This has expanded the possibilities of solving real world problems using computational learning frameworks. However, while gathering large quantities of unlabeled data is cheap and easy, annotating them with class labels entails significant human labor. The objective of this project is to develop a batch mode active learning scheme to select batches of informative samples (for manual annotation) from large quantities of unlabeled data. This tremendously reduces the labeling cost and also exposes the learner to the exemplar instances from the unlabeled set. Our current application is in the domain of biometric recognition. Due to the high frame rate of modern video cameras, the captured images have a high redundancy. Thus, batch mode active learning is of paramount importance in identifying the promising instances from such a superfluous set.