Illumination normalization for face recognition and uneven background correction using total variation based image models

We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L¹ model: minimizing the total variation of the output cartoon while subject to an L¹-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA mic roar ray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.