Robust thermal Face Recognition using Region Classifiers

This paper presents a robust approach for recognition of thermal face images based on decision level fusion of 34 di®erent region classi ̄ers. The region classi ̄ers concentrate on local variations. They use singular value decomposition (SVD) for feature extraction. Fusion of decisions of the region classi ̄er is done by using majority voting technique. The algorithm is tolerant against false exclusion of thermal information produced by the presence of inconsistent distribution of temperature statistics which generally make the identi ̄cation process di±cult. The algorithm is extensively evaluated on UGC-JU thermal face database, and Terravic facial infrared database and the recognition performance are found to be 95.83% and 100%, respectively. A comparative study has also been made with the existing works in the literature.