Pokerface: Partial order keeping and energy repressing method for extreme face illumination normalization

Abstract

We propose a new method called the Pokerface for extreme face illumination normalization. The Pokerface is a two-phase approach. It first aims at maximizing the minimum gap between adjacently-valued pixels while keeping the partial ordering of the pixels in the face image under extreme illumination condition, an intuitive effort based on order theory to unveil the underlying structure of a dark image. This optimization can be formulated as a feasibility search problem and can be efficiently solved by linear programming. It then smooths the intermediate representation by repressing the energy of the gradient map. The smoothing step is carried out by total variation minimization and sparse approximation. The illumination normalized faces using our proposed Pokerface not only exhibit very high fidelity against neutrally illuminated face, but also allow for a significant improvement in face verification experiments using even the simplest classifier. Simultaneously achieving high level of faithfulness and expressiveness is very rare among other methods. These conclusions are drawn after benchmarking our algorithm against 22 prevailing illumination normalization techniques on both the CMU Multi-PIE database and Extended YaleB database that are widely adopted for face illumination problems.