Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network

Hyeonsang Hwang,Eui Chul Lee

doi:10.1109/access.2020.3020142

Abstract

The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network.

Highlights

With the increasing popularity of virtual reality and headmounted displays (HMDs), there is growing interest in the security and user authentication of HMDs [1], [2]
residual network (ResNet) and deep ResNet have about 2.72% and 2.58% false reject rate (FRR) when false acceptance rate (FAR) are less than 0.1%, respectively
We proposed a novel convolutional neural networks (CNN) architecture for periocular authentication and an analysis method

Summary

INTRODUCTION

With the increasing popularity of virtual reality and headmounted displays (HMDs), there is growing interest in the security and user authentication of HMDs [1], [2]. Face-based authentication is a non-contact, camera-based method that has less inconvenience. It offers significantly reduced performance because of external factors, such as changes in illumination and resolution, and it requires. This is an unacceptable method in HMD environments where a display device must be worn on the face Another non-contact authentication method is iris recognition, which is highly accurate, unlikely to be damaged, and suitable in HMD environments [6]. The biometric authentication method of periocular imaging has recently been considered as a means of overcoming these problems [7]. C. Lee: Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network improve classification performance by using mid-level features. Lee: Near-Infrared Image-Based Periocular Biometric Method Using Convolutional Neural Network improve classification performance by using mid-level features. 2) The performance of the proposed method is verified using public databases and self-collected data, and it is compared with state-of-the-art methods in the field. 3) In the neural network, a new analysis method is proposed to find a step that contributes significantly to the results, and it is used in the analysis of the proposed network

RELATED WORKS

CONTRIBUTION OF BRANCH ANALYSIS METHOD

Findings

CONCLUSION