Performance Analysis of Smart Card-Based Fingerprint Recognition for Secure User Authentication

Gil, Youn-Hee; Chung, Yongwha; Ahn, Do-Seob; Moon, Jooho; Kim, Hakil

doi:10.1007/0-306-47009-8_7

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…To assign the verification steps to either the smart card or the card reader, we first evaluated the resource requirements of each step. Gil et al [22] reported that the preprocessing and extraction steps could not be executed in resource-constrained environments such as the smart card, so we assigned only the minutiae-matching step to the smart card.…”

Section: Fingerprint-based Match-on-cardmentioning

confidence: 99%

“…Central storage of biometric patterns leaves the system open to misuse of the biometric patterns: for example, the Big Brother problem. To protect against misuse, the database can be decentralized into millions of smart cards [12]- [22]. Most of the current implementations of this solution accomplish the biometric verification process solely outside of the smart card.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Memory-Efficient Fingerprint Verification Algorithm Using a Multi-Resolution Accumulator Array

Pan¹,

Gil²,

Moon³

et al. 2003

ETRI J

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Using biometrics to verify a person's identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. At the same time, improvements in VLSI technology have recently led to the introduction of smart cards with 32‐bit RISC processors. To gain maximum security in verification systems using biometrics, verification as well as storage of the biometric pattern must be done in the smart card. However, because of the limited resources (processing power and memory space) of the smart card, integrating biometrics into it is still an open challenge. In this paper, we propose a fingerprint verification algorithm using a multi‐resolution accumulator array that can be executed in restricted environments such as the smart card. We first evaluate both the number of instructions executed and the memory requirement for each step of a typical fingerprint verification algorithm. We then develop a memory‐efficient algorithm for the most memory‐consuming step (alignment) using a multiresolution accumulator array. Our experimental results show that the proposed algorithm can reduce the required memory space by a factor of 40 and can be executed in real time in resource‐constrained environments without significantly degrading accuracy.

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Section: Fingerprint-based Match-on-cardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Memory-Efficient Fingerprint Verification Algorithm Using a Multi-Resolution Accumulator Array

Pan¹,

Gil²,

Moon³

et al. 2003

ETRI J

Self Cite

View full text Add to dashboard Cite

show abstract

“…To assign the verification steps to the USB token or the host PC, we evaluate first the resource requirements of each step. Gil et al [10] reported that the Pre-processing and Extraction steps cannot be executed on the resource-constrained environments such as USB token. Thus, we determined the Minutiae Matching step is executed on the USB token.…”

Section: Match-on-tokenmentioning

confidence: 99%

“…With the central storage of the biometric pattern, there are open issues of misuse of the biometric pattern such as the "Big Brother" problem. To solve these open issues, the database can be decentralized into millions of security token such as smart card, USB token [8][9][10]. USB token is technologically identical to smart cards, with the exception of the interface to the computer.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the USB Token System for Fingerprint Verification

et al. 2003

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In the modern electronic world, the authentication of a person is an important task in many areas of day-to-day. Using biometrics to authenticate a person's identity has several advantages over the present practices of Personal Identification Numbers (PINs) and passwords. To gain maximum security in the verification system using biometrics, the computation of the verification as well as the store of the biometric pattern has to take place in the security token(e.g., smart card, USB token). However, there is an open issue of integrating biometrics into the security token because of its limited resources(processing power and memory space). In this paper, we describe our implementation of the USB token system having 206MHz StrongARM CPU, 16MBytes Flash memory, and 1MBytes RAM. Also, we describe a fingerprint verification algorithm that can be executed in the restricted environments. To meet the memory space specification and processing power of the security token, in fingerprint verification algorithm, we develop a data structure, called a multi-resolution accumulator array. Based on experimental results, we confirmed that the RAM requirement of the proposed algorithm is about 16 KBytes, and the Equal Error Rate(EER) is 1.7%. Therefore, our fingerprint verification algorithm can be executed in real-time on the developed USB token without degrading accuracy.

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“…The login authentication schemes based on biological features use a biometric of human beings that cannot change to carry out identity authentication. It is difficult for attackers to imitate these biometrics, such as iris [22,35], fingerprint [11], face [23], and so on. Some schemes (such as fingerprint and iris authentication methods) adopt static biometric authentication, which provides attacker with opportunities to log in illegally, thus threatening people's privacy and property security.…”

Section: Introductionmentioning

confidence: 99%

HandiText

Fang

Zhu

et al. 2020

ACM/IMS Trans. Data Sci.

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The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user's serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.

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Performance Analysis of Smart Card-Based Fingerprint Recognition for Secure User Authentication

Cited by 5 publications

References 3 publications

A Memory-Efficient Fingerprint Verification Algorithm Using a Multi-Resolution Accumulator Array

A Memory-Efficient Fingerprint Verification Algorithm Using a Multi-Resolution Accumulator Array

Implementation of the USB Token System for Fingerprint Verification

HandiText

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