Identity Verification Using Biometrics in Smart-Cities

Biometrics suggests a smart solution to keep the city safe. Installing a biometrics app on a mobile device facilitates identity recognition and verification instantaneously. Current work explores an authentication algorithm to address requirements of such memory restricted apps. A potential portion of periocular region, known as lower central periocular region, is examined to attain unconstrained authentication coupled with benefits of reduced template size. A novel computationally efficient feature extraction approach is employed over the region of interest using an efficient variation of conventional local binary pattern. The technique computes texture patterns over a dominant bit-plane, alternative to employing entire intensity image itself. Construction of the dominant bit-plane prior to feature extraction significantly simplifies operations required for texture pattern computations. The proposed methodology is tested on benchmark UBIRISv2 database and periocular images retrieved from high and low resolution imaging devices. Experimental results show an attainment up to 99.5% authentication accuracy in an unconstrained environment.