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Empirical comparisons of several preprocessing methods for illumination insensitive face recognition

Illumination variation is one of the bottlenecks of face recognition systems. Many approaches to coping with illumination variations have been proposed. They can be categorized into model-based and preprocessing-based. Although the model-based approaches seem better in theory, they commonly introduce more constraints, which make them not practical enough for real applications. On the other hand, the preprocessing approaches commonly exploit simple and efficient image processing techniques. Typical approaches based on image processing include histogram equalization (HE), histogram specification (HS), logarithm transform (Log), gamma intensity correction (GIC), and self-quotient image (SQI). We have performed extensive experiments to analyze and compare these methods empirically by evaluating them on three large-scale face databases: CMU-PIE database, FERET database; CAS-PEAL database. Our experimental results show that HE, HS and GIC can improve recognition performance for images both with and without illumination variation, while Log and SQI may decrease the recognition rate for face images without much illumination variation, though they may facilitate the recognition of face images with illumination variation.