Indexed on: 10 Mar '17Published on: 13 Jan '17Published in: Applied Soft Computing
This paper presents a new approach for face recognition under pose and illumination variations. The concept of information set is presented and the features based on this are derived using the Mamta-Hanman entropy function. The properties of an adaptive version of this entropy are given and nonlinear Shannon transform and Hanman transform which area higher form of information set are formulated. The information set based features and the nonlinear Shannon transform features are separately combined with the Pseudo-inverse Locality Preserving Projections (PLPP) for improving their effectiveness. The performance of the combined features is compared with that of the holistic approaches on four face databases (two FERET, one head pose image, and Extended Yale face database). The features from the combination of nonlinear Shannon transform and PLPP give consistent performance on the three databases tested whereas the well known features from the literature show good performance on one or two databases only.