A novel dice similarity measure for IFSs and its applications in pattern and face recognition

Abstract

In the theory of Intuitionistic fuzzy set (IFS), similarity measure is an effective instrument to measure similarity between IFSs. In this paper, we mention limitations of various existing similarity measures and propose a novel dice similarity measure for IFSs. Proposed dice similarity measure is based on inner product and overcomes limitations of existing similarity measures. In order to show the suitability and applicability of proposed dice similarity measure, we implement it on various classification problems of pattern recognition and medical diagnosis. Experimental results show that this does not only overcome limitations of existing similarity measures but also outperforms in pattern recognition and medical diagnosis problems. In this paper, we also propose an algorithm for face recognition problem using proposed dice similarity measure. This algorithm is demonstrated by an example and performance is compared with few existing methods for face recognition problems.