An Introduction to Information Sets with an Application to Iris Based Authentication

This paper presents the information set which originates from a fuzzy set on applying the Hanman-Anirban entropy function to represent the uncertainty. Each element of the information set is called the information value which is a product of the information source value and its membership function value. The Hanman filter that modifies the information set is derived by using a filtering function. Adaptive Hanman-Anirban entropy is formulated and its properties are given. It paves the way for higher form of information sets called Hanman transforms that evaluate the information source based on the information obtained on it. Based on the information set six features, Effective Gaussian Information source value (EGI), Total Effective Gaussian Information (TEGI), Energy Feature (EF), Sigmoid Feature (SF), Hanman transform (HT) and Hanman Filter (HF) features are derived. The performance of the new features is evaluated on CASIA-IRIS-V3-Lamp database using both Inner Product Classifier (IPC) and Support Vector Machine (SVM). To tackle the problem of partially occluded eyes, majority voting method is applied on the iris strips and this enables better performance than that obtained when only a single iris strip is used.

Video-Based Face Recognition with New Classifiers

An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.

Design of Hanman Entropy Network from Radial Basis Function Network

Different learning algorithms have been developed in the literature for training the radial basis function network (RBFN). In this paper, a new neural network named as Hanman Entropy Network (HEN) is developed from RBFN based on the Information set theory that deals with the representation of possibilistic uncertainty in the attribute/property values termed as information source values. The parameters of both HEN and RBFN are learned using a new learning algorithm called JAYA that solves the constrained and unconstrained optimization problems and is bereft of algorithm-specific parameters. The performance of HEN is shown to be superior to that of RBFN on four datasets. The advantage of HEN is that it can use both information source values and their membership values in several ways whereas RBFN uses only the membership function values.