A novel illumination-robust face recognition using statistical and non-statistical method

This paper proposes a novel illumination-robust face recognition technique that combines the statistical global illumination transformation and the non-statistical local face representation methods. When a new face image with arbitrary illumination is given, it is transformed into a number of face images exhibiting different illuminations using a statistical bilinear model-based indirect illumination transformation. Each illumination transformed image is then represented by a histogram sequence that concatenates the histograms of the non-statistical multi-resolution uniform local Gabor binary patterns (MULGBP) for all the local regions. This is facilitated by dividing the input image into several regular local regions, converting each local region using several Gabor filters, and converting each Gabor filtered region image into multi-resolution local binary patterns (MULBP). Finally, face recognition is performed by a simple histogram matching process. Experimental results demonstrate that the proposed face recognition method is highly robust to illumination variation as exhibited in the real environment.

Research highlights
► This paper proposes a novel illumination-robust face recognition technique.
► It combines global illumination transformation and local face representation method.
► It uses a statistical bilinear model-based indirect illumination transformation.
► It uses a concatenation of the histograms of the non-statistical MULGBP.
► It shows a highly robustness to illumination variation in the real environment.