Abstract: In biometric research and many security areas, it is very difficult task to match optical face images to infrared face images. infrared and optical face images captured by different devices such as infrared imaging device and optical imaging device large difference exist between infrared face images and optical face images because they belongs to multiple modality. Converting the samples of multimodality into common feature space is the main objective of this project. The new method has been developing for identification of heterogeneous face identification. Training set contains the images from different modalities. Initially the infrared image is preprocessed by applying Gaussian filter, difference of Gaussian and CSDN filters are apply on infrared face image. After preprocessing next step to extracting the feature by using LBP(local binary pattern) feature extraction then relevance machine classifier is used to identify the best matching optical image from the corresponding infrared images from the optical images dataset.
Keywords: Image matching, infrared and optical face images, LBP (local binary pattern), RVM (relevance machine classifier).