DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Ahmad S Tarawneh,Abdullah Almuhaimeed,Esra’A Alkafaween,Malek Alrashidi,Ahmad B Hassanat,Bayan Sarayrah,Ghada A Altarawneh,Sami Mnasri,Mansoor Alghamdi

doi:10.3390/electronics11040513

Abstract

Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

Highlights

This study aims at providing an investigation structure for the use of deep learning in supporting finger-knuckle print (FKP) recognition using VGG-19 (f6 and f7); thereof, our study intends to achieve the following objectives: 1. Determine the extent to which deep learning can support FKP recognition using the deep VGG-19 method
The Artificial neural network (ANN) is the best classifier to be used for deep features extracted using VGG-19, if it is provided with the reduced version of the features, otherwise, i.e., if it is applied on the original pure deep features, which obtained from the VGG-19 layer 6 or 7 or any merging of them both, the training time would be unacceptably long
Deep learning network VGG-19 is investigated to be used for FKP identification or authentication system, using the deep features collected at layers 6 and 7, with and without dimensionality reduction tools such as the principal component analysis (PCA), merging both deep features (F6 and fully connected layer 7 (F7)) is investigated using different rules such as average, maximum, and minimum

Summary

Introduction

Automated identification solutions have become critical for security and privacy in today’s digitally linked world [1]. Biometrics is a type of person recognition method; it is a security solution that differs from typical authentication and identification techniques such as passwords, ID cards, and PIN codes [2]. Biometrics are techniques that use automated ways to objectively validate a system utilizing biological features. It uses physiological or behavioral aspects of humans as a means of authenticating personal identity [3]. Physiological characteristics include those retrieved from the human body, such as iris [4], faces [5,6], retinas, veins [7], fingerprints [8–11], palm prints, finger knuckle print, and DNA, ECG [12–15], while behavioral characteristics include voice, stride, signature, and keystroke [16–20]. Biometrics traits are commonly employed in systems such as Security in IoT [21–25], e-banking [26–29], cloud security [30–33], access control [34–37], network security systems [38–42], and ID cards [43–47], and other applications related to IoT [48–52]

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