Authentication of Identical Twins Using Tri Modal Matching

With the increase in the number of twin births in recent decades, there is a need to develop alternate approaches that can secure the biometric system. In this paper, a new approach for identifying identical twins on the basis of a multimodal identification system that uses three different features namely face, finger print and lip print to identify people. A newly developed multimodal biometric system possesses a number of unique qualities, starting from utilizing Kernel Similarity with Euclidean distance methods for face matching, Possibilistic Fuzzy C-means clustering (PFCM) for fingerprint matching and Fixed K-means Clustering features for lip print matching and fused the information for effective recognition and authentication. The importance of considering these boundary conditions, such as twins, where the possibility of errors is maximum will lead us to design a more reliable and robust security system. The proposed approach is tested on a real database consisting of 429 pair of identical twin images and shows promising results. The Receiver Operating Characteristics also shows that the proposed method is superior compared to other techniques, subsequently, the experimental results prove the ability of proposed method to recognize a pair of identical twins at ease.