Modified Conditional Generative Adversarial Network-Based Optical Blur Restoration for Finger-Vein Recognition

Jiho Choi,Se Hyun Nam,Muhammad Owais,Kang Ryoung Park,Kyoung Jun Noh,Se Woon Cho

doi:10.1109/access.2020.2967771

Jiho Choi, Se Hyun Nam + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2967771

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Abstract

Among the existing biometrics methods, finger-vein recognition is beneficial because finger-veins patterns are locate under the skin and thus difficult to forge. Moreover, user convenience is high because non-invasive image capturing devices are used for recognition. In real environments, however, optical blur can occur while capturing finger-vein images du to both skin scattering blur caused by light scattering in the skin layer and lens focus mismatch caused by finger movement. The blurred images generated in this manner can cause severe performance degradation for finger-vein recognition. The majority of the previous studies addressed the restoration method o skin scattering blurred images; however, only limited studies have addressed the restoration of optically blurred images. Even the previous studies on the restoration of optical blur restoration have performed restoration based on the estimation of the accurate point spread function (PSF) for a specific image-capturing device. Thus, it is difficult to apply these methods to finger-vein images acquired by different devices. To address this problem, this paperproposes a new method for restoring optically blurred finger-vei images using a modified conditional generative adversarial network (conditional GAN) and recognizing the restored finger-vein images using a deep convolutional neural network (CNN). The results of the experiment performed using two open databases, the Shandong University homologous multimodal traits (SDUMLA-HMT) finger-vein database and Hong Kong Polytechnic University finger-image database (version 1) confirmed that the proposed method outperforms the existing methods.

Highlights

Among biometric technologies such as face, iris, fingerprint, and finger-vein recognition, finger-vein recognition has the following benefits [1]. (1) As a finger-vein is hidden inside the body and is typically invisible to the human eye, it is difficult to be forged or stolen. (2) The non-invasive image capturing ensures both convenience and cleanliness and is more acceptable for the user. (3) Because a person has ten fingers, if something unexpected happens in one finger, the other fingers can be used for authentication
convolutional neural network (CNN) can be applied to finger-vein images acquired from different environments. Considering these reasons, in this study, we propose a method of performing optical-blurred finger-vein image restoration using a conditional generative adversarial network [10]
To address the problem of finger-vein recognition performance degradation due to optical blur, this study proposed a method for restoring optically blurred finger-vein images using modified conditional generative adversarial network (GAN) and recognizing the restored images using deep CNN

Summary

Introduction

Among biometric technologies such as face, iris, fingerprint, and finger-vein recognition, finger-vein recognition has the following benefits [1]. (1) As a finger-vein is hidden inside the body and is typically invisible to the human eye, it is difficult to be forged or stolen. (2) The non-invasive image capturing ensures both convenience and cleanliness and is more acceptable for the user. (3) Because a person has ten fingers, if something unexpected happens in one finger, the other fingers can be used for authentication. In real environments blurred images can be generated while capturing. Finger-vein images due to the focus mismatch of the finger-vein acquisition camera lens and light scattering in the skin layer. Because of the nature of the near-infrared (NIR) light used to acquire the images of the finger-veins present under the skin, skin scattering blur that reduces the sharpness of the acquired finger-vein images caused by light scattering in the tissues and moisture in the skin frequently occurs. Numerous studies have been conducted to improve recognition accuracy by solving skin scattering blur [2]–[8]. Motion blurring rarely occurs in the input images because the images are captured while the fingers are fixed, to a certain extent, on the finger-vein image capturing devices.

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