Properties of information sets and information processing with an application to face recognition

This paper presents the properties of information sets that help derive local features from a face when partitioned into windows and devises the information rules from the generalized fuzzy rules for information processing that helps match the unknown test face with the known for authenticating a user. information set is constituted from the information values that result from representing the uncertainty in a type-1 fuzzy set by Hanman–Anirban entropy function. The information values are shown to be the products of information sources (gray levels) in a window and their membership function values. The Hanman filter (HF) is devised to modify the information values using a cosine function whereas the Hanman transform (HT) is devised to evaluate the information source values based on the information obtained on them. Three classifiers, namely the inner product classifier, normed error classifier, and Hanman classifier are formulated. The two feature types based on HF and HT are tested on the AT&T (ORL) database, which contains pose variations in the face images and two other face databases: Indian face Database (IIT Kanpur) and UMIST (Sheffield) using new as well as known classifiers like Euclidean distance- based, Bayesian, and support vector machine classifiers.