Face recognition with statistical Local Binary Patterns

In this work, we present a novel algorithm for face recognition named statistical local binary patterns (sLBP). This is a further development of original local binary pattern algorithm. Our method is applied for face recognition under visual light environment dealing with dramatically illumination varying on faces After a statistical analysis on the distribution probability of the gray-level difference values between neighbor pixels, a mapping function is proposed to encode a wide range of these values into three binary bits. Three extension LBP layers are then generated Finally the uniform pattern histograms of all these layers in every divided region are concatenated as an enhanced local feature vector of the face image. Experimental results on FERET face database show considerable effectiveness and robustness of our proposed method.