Using a Probabilistic Neural Network for lip-based biometric verification

In classical recognition techniques only raw features of objects are employed. Our approach allows use the composed features - so called Sim coefficients and landmarks which determine the area where biometric features should be searched. Biometric composed features are associated with appropriate similarity coefficients. Such approach brings significant advantages - recognition level of objects is higher compared to method based on the raw data. In this paper, a novel and effective lip-based biometric recognition approach with the Probabilistic Neural Network (PNN) is proposed. Lip based recognition has been less developed than the recognition of other human physical attributes such as the fingerprint, voice patterns, blood vessel patterns, or the face. For this reason, achieved results on this field are still improved and new recognition techniques are searched. Results achieved by PNN were improved by the Particle Swarm Optimization (PSO) technique. In the first step, lip area is restricted to a Region Of Interest (ROI) and in the second step, features extracted from ROI are specifically modeled by dedicated image processing algorithms. Extracted lip features are then an input data of neural network. All experiments were confirmed in the ten-fold cross validation fashion on three diverse datasets, Multi-PIE Face Dataset, PUT database and our own faces dataset. Obtained in researches result show that proposed approach achieves an average classification accuracy of 86.95%, 87.14%, and 87.26%, on these three datasets, respectively. Announced results were verified in the comparative studies and confirm the efficacy of the proposed lip based biometrics learned by PSO technique. (C) 2017 Elsevier Ltd. All rights reserved.