Interest filter vs. interest operator: Face recognition using Fisher linear discriminant based on interest filter representation

This paper introduces a novel Fisher discriminant classifier based on the interest filter representation for face recognition. Our interest Fisher classifier (IFC), which is robust to illumination and facial expression variability, applies the Fisher linear discriminant (FLD) to an augmented interest feature vector derived from interest filter representation of face images. The novelty of this paper comes from our proposed interest filter: the interest operator can reveal the local activity of the images but suffer from some drawbacks and we improve the capability of the interest operator and propose a multi-orientation and multi-scale interest filter. In particular, we carry out comparative studies of different similarity measures applied to various classifiers. We also perform comparative experimental studies of various face recognition schemes, including our novel IFC method, the Eigenfaces and the Fisherfaces methods, the combination of interest operator and the Eigenfaces method, the combination of interest operator and the Fisherfaces method, the Eigenfaces on the augmented interest feature vectors and other popular subspace methods. The feasibility of the new IFC method has been successfully tested on two data sets from the FERET and AR databases. The novel IFC method achieves the highest accuracy on face recognition on both two datasets.