Smartphone based robust iris recognition in visible spectrum using clustered K-means features

Smartphones and tablet computers are being actively studied for the performance of biometric recognition in visible spectrum. Owing to robust performance of iris recognition, many works have investigated the performance in visible spectrum. Increasing popularity of iris recognition in the visible spectrum has further resulted in using smartphones for the same. The extraction of robust features for visible spectrum iris recognition is vital to meet the expected accuracy of recognition. In this work, we explore K - means clustering based feature extraction to obtain robust features. K-means clustering is a fast alternative training method that is not computationally expensive and can easily be extended to large scale systems. The robust features extracted serves best for the unconstrained iris recognition on smartphones in visible spectrum. The proposed feature extraction technique has been extensively evaluated on publicly available smartphone iris database from BIPLab. The best Equal Error Rate of 0.31% is achieved using the proposed technique on images captured using iPhone in indoor scenario.