Fingerprint Recognition Using a Transfer Learning Method

Lecture Notes in Computer Science, 2001

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs), and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features which can be integrated in the SVMs. SVMs are combined with a new error correcting code scheme which, unlike previous systems, can also exploit information contained in ambiguous fingerprint images. Experimental results indicate the benefit of integrating global and structured representations and suggest that SVMs are a promising approach for fingerprint classification.

SMART MOVES JOURNAL IJOSCIENCE, 2019

A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. Authentication system like fingerprint recognition is most commonly used biometric authentication system. Fingerprint method of identification is the oldest and widely used method of authentication used in biometrics. There are several reasons like displacement of finger during scanning, environmental conditions, behavior of user, etc., which causes the reduction in acceptance rate during fingerprint recognition. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. Fingerprint Recognition system designed uses various techniques in order to reduce the False Acceptance Rate (FAR) and False Rejection Rate (FRR) and to enhance the performance of the system. This paper reviews the fingerprint classification including feature extraction methods and learning models for proper classification to label different fingerprints. A ...

Springer eBooks, 2009

First, an overview of the state of the art in fingerprint recognition is presented, including current issues and challenges. Fingerprint databases and evaluation campaigns, are also summarized. This is followed by the description of the BioSecure Benchmarking Framework for Fingerprints, using the NIST Fingerpint Image Software (NFIS2), the publicly available MCYT-100 database, and two evaluation protocols. Two research systems are compared within the proposed framework. The evaluated systems follow different approaches for fingerprint processing and are discussed in detail. Fusion experiments involving different combinations of the presented systems are also given. The NFIS2 software is also used to obtain the fingerprint scores for the multimodal experiments conducted within the BioSecure Multimodal Evaluation Campaign (BMEC'2007) reported in Chap. 11.

Fingerprint Analysis is one of the efficient biometric tool which is highly used to verify the identity of the user. Artificial neural networks consist of various sets of neurons which resembles the neurons in the human brains. ANN is most widely used in the fingerprint analysis because of quick learning Techniques. Usually the ANN is trained to determine the characteristics that will be occurring in the fingerprint and extraction of those characteristics. In the training phase the extracted characterizations are compared with the templates that will be present in the stored database. A wavelet is used to divide a continuous signal into a scalable component, this technique of wavelets help to determine and differentiate the mordant characteristics of the fingerprint.

Sensors

Recent developments enable biometric recognition systems to be available as mobile solutions or to be even integrated into modern smartphone devices. Thus, smartphone devices can be used as mobile fingerprint image acquisition devices, and it has become feasible to process fingerprints on these devices, which helps police authorities carry out identity verification. In this paper, we provide a comprehensive and in-depth engineering study on the different stages of the fingerprint recognition toolchain. The insights gained throughout this study serve as guidance for future work towards developing a contactless mobile fingerprint solution based on the iPhone 11, working without any additional hardware. The targeted solution will be capable of acquiring 4 fingers at once (except the thumb) in a contactless manner, automatically segmenting the fingertips, pre-processing them (including a specific enhancement), and thus enabling fingerprint comparison against contact-based datasets. For ...

Contemporary Engineering Sciences

Biometrics, as a computer science field, can be understood as the discipline that study how to generate computer models of the physical (e.g. hand geometry, fingerprints, iris and so on) and behavioral (e.g. signature; a kind of behavior pattern) characteristics of the human being for single or several individuals identification. Usually, these characteristics are used to provide authentication information for security systems. However, some of these characteristics are hard to obtain in a properly way and it is necessary to use several algorithms both to process them and to use them on a security systems. In this sense, in this paper it is presented an overview of some Machine Learning approaches for biometric pattern recognition.

International Journal of Information Technology

Biometric features are useful for unique identification, authentication, and security applications. Among all biometric features, fingerprints are the most commonly used because they contain ridges and valleys. There are challenges in recognizing child or infant fingerprints since the ridges are not mature as the hands are covered with a white substance and acquisition of fingerprint images is difficult. In the context of COVID-19 pandemic, contactless fingerprint acquisition gains importance as it is not infectious especially with children. In this study, a Convolutional Neural Network (CNN) based children recognition system named Child-CLEF, that uses Contact-Less Children Fingerprint (CLCF) dataset acquired using a mobile phonebased scanner is proposed. The quality of captured fingerprint images is enhanced using a hybrid image enhancement method. Furthermore, the minutiae features are extracted using the proposed Child-CLEF Net model and the identification of children is made using a matching algorithm. The proposed system is tested with a self-captured children fingerprint dataset, CLCF and publicly available PolyU fingerprint dataset. It is found that the proposed system outperforms the existing fingerprint recognition systems in terms of accuracy and equal error rate.

Biometric fingerprint verification and identification have been extensively used in real life applications as an authentication and access control mechanism. Newer contactless fingerprint scanning technology offers high convenience and hygiene, especially in the view of COVID-19. Attackers still challenge the biometric security offered by contactless scanners by illegitimate acquisition of the user's fingerprint through various spoofing methods. Therefore, detection of contactless fingerprint spoof is on the urge to protect the biometric security systems. The existing solutions to contactless fingerint spoof detection face the lacuna of considering limited fingerprint features leading to low spoof detection accuracy. In this study, this issue has been addressed and CLNet (Contact Less Network) approach is proposed to detect the spoofness in contactless fingerprints. The proposed CLNet is a deep neural network approach utilizing contactless fingerprint images followed by a transfer learning approach called SpoofDetNet which is based on the MobileNetV2 model. The motivation for the development of the SpoofDetNet is to create a spoof detection method viable for contactless fingerprint images as well as contact-based fingerprint images which stand strong among state-of-the-art models. We created a Spoofed-Contactless Adult Fingerprint (S-CLAF) dataset with live and spoof contactless fingerprint images. The CLNet approach was trained and tested on S-CLAF dataset and it achieved an accuracy of 99.07% across all spoofed materials. Furthermore, the proposed approach was tested using LivDet 2015 benchmark dataset and IIT Bombay touchless fingerprint dataset achieving accuracy of 98.32% and 99.38% respectively. It is evident from the experimental results that the proposed CLNet outperforms the state-of-the-art fingerprint spoof detection methods.

Pattern Analysis & Applications, 2004

Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.

Knowledge-Based Systems, 2015

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.