We present a comprehensive performance study of multiple appearance-based face recognition methodologies, on visible and thermal infrared imagery. We compare algorithms within the same imaging modality as well as between them. Both identification and verification scenarios are considered, and appropriate performance statistics reported for each case. Our experimental design is aimed at gaining full understanding of algorithm performance under varying conditions, and is based on Monte Carlo analysis of performance measures. This analysis reveals that under many circumstances, using thermal infrared imagery yields higher performance, while in other cases performance in both modalities is equivalent. Performance increases further when algorithms on visible and thermal infrared imagery are fused. Our study also provides a partial explanation for the multiple contradictory claims in the literature regarding performance of various algorithms on visible data sets.
Face recognition; thermal image; noise influence.