Robust and computationally efficient framework for human recognition using ear biometrics

Abstract

The research work titled Robust and Computationally efficient framework for human recognition using Ear Biometrics is focused on the development of a robust and computationally efficient human recognition system using ear biometrics by extracting robust Unique Mapped Real Transform (UMRT) texture features, Shape features and Histogram features from ear modalities. Human recognition can be successfully performed using several life science metrics in our day-to-day life with the help of recent state-ofart techniques in human recognition. Biometrics deals with human recognition using computational life science measurements and the deployment of biometrics has become the most common and the indispensable part of our diversified dayto- day affairs. Due to the advances in biometrics technology, the inappropriate use of debit or credit cards in commercial applications as well as impersonation at airports, train stations and that at polling stations during elections can be drastically reduced. During few sensitive and challenging scenarios, harmless newlineauthenticated users have to claim their identities against malicious, impersonating users. Such difficulties faced by public during invasive acquisition can be reduced by deploying non-invasive acquisition of biometric modalities. Modern biometric systems have already started deploying noninvasive biometric recognition systems by acquiring biometric modalities such as face, ear, iris etc, in a non-invasive manner. The main reason for developing a non-invasive biometric system is to acquire the biometric modalities without affecting or disturbing the subject. newline newline