FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

Rachida Tobji,Naeem Ayoub,Wu Di

doi:10.3390/app9102042

Rachida Tobji, Naeem Ayoub + Show 1 more

Open Access

https://doi.org/10.3390/app9102042

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Abstract

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

Highlights

Nowadays iris recognition is becoming more important security feature in biometric security systems
We propose an approach based on the vision deep neural networks
A group of researchers led by Geoff Hinton at the University of Toronto, Canada, found a way to train the neural network without falling into the local minima problem [9]

Summary

Introduction

Nowadays iris recognition is becoming more important security feature in biometric security systems. Practical observation through an optical system only allows detecting the edges macroscopic, and not to go down to the level of the elementary tubes These random patterns of the iris are unique for each individual: they constitute somehow a human bar code from the filaments, hollows and streaks in the colored circles that surround the pupil of each eye. A group of researchers led by Geoff Hinton at the University of Toronto, Canada, found a way to train the neural network without falling into the local minima problem [9]. In this era, graphics processing units (GPUs) were developed, which facilitated the handling of images on the PC.

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