Multi-spectral Face Recognition System

Face recognition is being actively pursued as a research area since the last five decades [1], due to the increase in crime rates and health rates, especially due to cases where security systems fail e.g. in case of disguise [2]. Therefore, this issue is worth solving to help control the crime rate and make the people feel more secure by identifying the actual identity of a subject by taking advantage of the visible and thermal imaging domain. The main challenge to the face recognition process is the variations like illumination, pose, expression and disguise [3]. The proposed framework will help in solving this problem using intensityhist and RLBP (ITR) features for classifying a facial patch as usable or unusable, local binary patterns (LBP) for feature extraction related to facial recognition purposes and mahalanobis cosine as the distance measure technique. The proposed framework is tested using a multi-spectral facial dataset (I2BVSD), which contains images form 75 different subjects in both, the visible and thermal domain. Results obtained by the proposed framework are better than reported frameworks on other datasets for face recognition process, and better as compared to the Anavrta [4] framework which is also tested on this (I2BVSD) dataset. This methodology can be employed to identify febrile subjects at places with innumerous numbers of people like airports, preventing the spread of pandemic situations.