Fingerprint matching, spoof and liveness detection: classification and literature review

Ali, Syed Ahmad; Khan, Muhammad Aamir Shafique; Aslam, Ahmed Sohail

doi:10.1007/s11704-020-9236-4

Cited by 15 publications

(8 citation statements)

References 83 publications

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“…In the Fingerprint-based authentication system, a hierarchy of three levels of features, namely Level 1 (Pattern), Level 2 (Minutiae Points), and Level 3 (Pores and ridge Shape), is used during verification and identification [39]. The fingerprint Matching techniques are generally classified into Correlation-based, minutiae-based, and non-minutiae based.…”

Section: Authentication Based On Fingerprintmentioning

confidence: 99%

Performance Analysis of Authentication system: A Systematic Literature Review

Singla

Verma

2023

Preprint

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Data authentication is vital nowadays, as the development of the internet and its applications allow users to have all-time data availability, attracting attention towards security and privacy and leading to authenticating legitimate users. We have diversified means to gain access to our accounts, like passwords, biometrics, and smartcards, even by merging two or more techniques or various factors of authentication. This paper presents a systematic literature review of papers published from 2010 to 2022 and gives an overview of all the authentication techniques available in the market. Our study provides a comprehensive overview of all three authentication techniques with all the performance metrics (Accuracy, Equal Error Rate (EER), False Acceptance Rate (FAR)), security, privacy, memory requirements, and usability (Acceptability by user)) that will help one choose a perfect authentication technique for an application. In addition, the study also explores the performance of multimodal and multi-factor authentication and the application areas of authentication.

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Section: Authentication Based On Fingerprintmentioning

confidence: 99%

Performance Analysis of Authentication system: A Systematic Literature Review

Singla

Verma

2023

Preprint

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“…The DL models have proven to be effective, and superior compared to the state-of-the-art approaches for biometric recognition systems, especially in AFRS. According to the review conducted by [19], DL approach is observed to be heavily investigated from 2012 to 2019. The authors argued that this will remain the active direction for the fingerprint research in the future.…”

Section: Open Accessmentioning

confidence: 99%

“…The DL based fingerprint algorithms and techniques are considered the best approach producing state-of-the-art results better than the conventional machine Journal of Artificial Intelligence and Systems learning algorithms, fuzzy logic, latent, image enhancement, holistic, and template matching as discussed in [19]. There are wide applications of DL to analyze fingerprint images for various applications tasks with proven performances better than the traditional methods.…”

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

“…A DL based study by [115] obtained over 90% of presentation attack success rate when FMR was 0.1% using skeleton image recovered from the leaked fingerprint template. Although minutiae based methods have consistently proven effective in biometric research [19], however, vulnerable features such as fingerprint type feature, orientation map, and skeleton image in terms of security should be avoided when developing fingerprint biometric systems [115]. Also, designing security measures in biometric systems while at the same time maintaining the recognition accuracy of the systems is still being explored [29] This study was restricted to uni-modal biometric (fingerprint biometric), however, studies have been proposed to take advantage of the strengths of the various biometric features through fusion of two or more biometrics to build a more accurate and secured identification also known as multimodal biometric systems [125][126][127].…”

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

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Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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“…We remain conscious that this represents only a snapshot of the research activity in the biometric field. For a complete overview we invite to refer to the third edition of the handbook of fingerprint recognition [ 8 ] and a recent review paper [ 9 ]. Deep learning approaches currently outperform other approaches (e.g.…”

Section: Introductionmentioning

confidence: 99%