Iris recognition using machine learning from smartphone captured images in visible light

摘要

This work shows the applicability and feasibility of different machine learning techniques on iris recognition from smartphone captured eye images. First, the iris is localized using the popular Daugman's method and the eyelids are suppressed with canny edge detection technique. Then normalization of the extracted iris region is performed in a novel way by setting an adaptive threshold. Next, the normalized image is decomposed using Haar wavelets to obtain the feature vectors. Histogram equalization is performed for better classification accuracy. After that, different classifiers are trained using the extracted feature vectors which yield about 99.7% accuracy for training and 97% accuracy for testing. Finally, the results are compared with other previously applied methods on the same dataset and it is found that the proposed method outperforms most of them.