A Novel Method of Face Recognition Using Lbp, Ltp And Gabor Features

Face recognition has received a great deal of attention from the scientific and industrial communities over the past several decades owing to its wide range of applications in information security and access control, law enforcement, surveillance and more generally image understanding. In this paper we combine KLDA(combination of LBP and GABOR features) with gradient face features(which are more resistive to the noise effects) for more effective recognition process. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and (iii) we further improve robustness by adding Kernel PCA feature extraction and incorporating rich local appearance cues from two complementary sources - Gabor wavelets and LBP - showing that the combination is considerably more accurate than either feature set alone.