Encoding and decoding local binary patterns for harsh face illumination normalization

In this work, we propose a new illumination normalization technique based on a simple, yet widely used descriptor: local binary patterns (LBP). We capitalize on the fact that LBP retains tolerance to illumination changes and use the LBP mapping to remove illumination variations cast on face images. Through learning a reverse mapping from the LBP domain to the pixel domain, we are able to recover the illumination normalized face with high fidelity. The reverse mapping step is made possible via a joint dictionary learning framework between the LBP domain and the pixel domain. The illumination normalized faces using our proposed LBP encoding and decoding method 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 nearest-neighbor classifier. These conclusions are drawn after benchmarking our algorithm against 22 prevailing illumination normalization techniques on Extended YaleB database which has been widely adopted for challenging face illumination problems.