ABSTRACT

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.

EXISTING SYSTEM

Several works have been done with the publicly available datasets UBIRISv1 [5], UBIRISv2 [6], MICHE [7] etc. containing iris images in visible light spectrum. The challenges of iris recognition associated with unconstrained iris images in visible light were discussed by Prenatal [8]. Noisy iris images and independent segmentation and noise recognition procedures are most likely the sources of errors. Santos et al. [9] explored best illumination configurations for visible light iris images. Algorithms offered more than 95% accuracy for the dataset. Machine learning techniques have also been proved to be very successful in iris recognition. A study from De Marsicoet al. [3] compared different machine learning techniques in iris recognition. In these studies, they used mostly CASIA-Iris [15] dataset which is created from images taken with NIR camera. Among different approaches, Rai and Yadav [16] were able to obtain 99% accuracy with a combination of Support Vector Machines and Hamming distance.

PROPOSED SYSTEM

In the proposed system we investigate further on the use of machine learning techniques on iris recognition using smartphone captured iris images in visible light spectrum. In order to do so we develop a complete segmentation and feature extraction technique and try to use same set of extracted features to train different classifier. Finally, we compare the classification accuracy of the trained classifiers and decide whether the machine learning techniques are feasible in case of smartphone captured database. 

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10