LAUSR

Facial recognition is a popular biometric technique to recognize an individual by comparing the facial features of a given photograph or a sketch to the digitally stored photographs. One of the important applications of facial recognition is to determine the identity of criminals through their hand-drawn or composite sketches. Despite of the development made in sketch-based facial recognition, available approaches are facing various challenges. The component-based approach (CBA) measures the similarity between each facial component of a sketch and a mugshot photograph. The major challenge in this approach is to determine which facial components are crucial in the identification process. Certain facial components provide better recognition clue than others while matching with mugshot photographs and considerably accurate identification results could be achieved to incorporate such crucial components in the recognition process. In this article, we propose a novel methodology which is based on computable weights to find the most discriminative facial components by using the Weighted Component-Based Approach (WCBA). The weight vector is used during the similarity score measurement to enhance the accuracy and performance of the facial recognition system. Experimental results on matching 50 facial images from 1193 subjects of Multiple Encounter Dataset II (MEDS-II) and 85 facial images from CHUK face sketch database (CUFS) show that the proposed method achieves promising performance (accuracies of 58.33% and 88.23%, respectively) as compared to other leading facial recognition techniques (accuracies of 52% and 80%). We believe our prototype approach will be of great value to law enforcement agencies in the apprehension of culprits in a timely fashion.