Multispectral Infrared Face Recognition: a comparative study

Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear dimensionality reduction techniques like PCA and LDA. In the thermal infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. In this work we introduce the use of non linear dimensionality reduction techniques and a probabilistic Bayesian technique for infrared face recognition. These techniques permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. A comparative study is conducted in order to evaluate the performance of the proposed techniques for infrared face recognition. Experimental results show that the non linear and probabilistic techniques are promising and lead to interesting results in the infrared spectrum.