A new interval type 2 fuzzy-based pixel wise information extraction for face recognition

A new efficient and robust approach based on pixel wise information extraction and fuzzy logic concept is presented here for the application of face recognition (FR). As all the pixels of an individual face image do not participate equally in identifying a face image from a given set of classes. Therefore, in this paper, we use a new interval type 2 fuzzy based pixel wise information extraction (NIntTy2FPIE) on input face images for computing the pixel wise association of individual pixels of a face image in a given dataset. Next, computational cost is reduced by principle component analysis (PCA) and classification is done using a variant of nearest neighbour classifier (NNC), called k-NNC. Experiments performed on ORL, Yale and Georgia Tech and AR face database show that our method outperforms with many state-of-art methods and also proves that the proposed method with k-NNC is much more efficient and robust.