Transformed Secure Feed Forward Supervised Learning Method for Authentication in Multi-Model Biometric System

Abstract

Biometric is an automated detection of the characteristics of an individual on the basis of the biological and social features. Detection of the uni-modal biometric system is based on the biometric data of an individual. Some issue of distortion level spoofing threats are more accessible to biometric data. Some of the issues overcome by multimodal biometric scheme in which signature of the biometric data are determine for better security of the data. Multimodal biometric is used on variety of the application areas which are human computer interface, detection of the sensor through unique method. The physical and social characteristics are used for the identification of an individual using multimodal biometric system. Multi-model biometric system applications are security system developed in banking sectors, business phase and Industry (MNC) companies. In existing work, using ESVM method to recognize the biometric traits and problem occurs in existing phase is distortion and degrades the image quality present and reduces the recognition rate and high error rates. In proposed research, determined the biometric features finger print, face and iris through CASIA dataset. Then, distortion rate is recognised through salt and pepper method and removal of interference using filtration technique. After that, discrete wavelet transformation is used for the extraction of the features of the biometric system through face, fingerprint and eye that determine the graphical features. Along with that, feed forward neural network algorithm developed for classification and recognition of multi modal biometric behaviour characteristics. The Encrypted NN method conducts simulation work on the metrics like as a recognition rate, true positive rate and computation time. The experimental results demonstrate that Encrypted NN method is able to enhance the image quality, recognition rate and TPR and reduces the computational time of Multi-model Biometric System when compared with existing work and simulation tool used MATLAB 2016a.