A novel approach for palm print recognition using entropy information features

Abstract

This paper presents a work about palm print recognition using fuzzy entropy. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept for modelling a palm print recognition system. The measure of uncertainty is adopted as a measure of information. Hence, the measures of fuzziness are known as fuzzy information measures. The measure of information is used to measure the uncertainty. Hence, fuzzy information measures are used to measure the fuzziness. The measure of a quantity of fuzzy information obtained from a fuzzy set or fuzzy system is known as fuzzy entropy. Since no probabilistic concept is needed to define fuzzy entropy, classical Shannon entropy is quite different from it. Further, Shannon entropy contains the randomness uncertainty (probabilistic) whereas fuzzy entropy contains vagueness and ambiguity uncertainties. The concept of membership function is used to define fuzzy entropy. The properties of fuzzy entropy have become popular and are being used very frequently to define any new fuzzy entropy. The fuzzy entropy function used in this paper is defined using the new random membership function μ. Previous works in palm print recognition use standard methods for feature extraction. By using standard techniques, the recognition rate was not as expected. Incorporating fuzzy concept to derive the features of palm print is the motivation for this work. The proposed work uses fuzzy membership function to extract the maximum information out of palm print as it contains lot of uncertainties. The fuzzy membership function developed in the proposed work is applied in the fuzzy entropy function to generate the new entropy information features which when tested with the standard classifier using support vector machine gives the encouraging result in terms of recognition rate. The proposed work was done using COEP (College of Engineering, Pune) database. It can be noted that palm print recognition is an important modality in biometrics for security purposes, as its use in practical life cannot be avoided. The palm print image contains lot of undefined and vague information and the best method to take out the maximum information out of the palm print image is the use of fuzzy logic concepts. This feature also motivated us for the proposed work.