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Fingerprint recognition

About: Fingerprint recognition is a research topic. Over the lifetime, 11538 publications have been published within this topic receiving 145445 citations. The topic is also known as: fingerprint authentication.


Papers
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Patent
09 Sep 2014
TL;DR: In this paper, an electronic device with a display and a fingerprint sensor displays a fingerprint enrollment interface and detects, on the fingerprint sensor, a plurality of finger gestures performed with a finger.
Abstract: An electronic device with a display and a fingerprint sensor displays a fingerprint enrollment interface and detects, on the fingerprint sensor, a plurality of finger gestures performed with a finger. The device collects fingerprint information from the plurality of finger gestures performed with the finger. After collecting the fingerprint information, the device determines whether the collected fingerprint information is sufficient to enroll a fingerprint of the finger. When the collected fingerprint information for the finger is sufficient to enroll the fingerprint of the finger, the device enrolls the fingerprint of the finger with the device. When the collected fingerprint information for the finger is not sufficient to enroll the fingerprint of the finger, the device displays a message in the fingerprint enrollment interface prompting a user to perform one or more additional finger gestures on the fingerprint sensor with the finger.

475 citations

Proceedings ArticleDOI
08 Nov 2003
TL;DR: The fundamental insecurities hampering a scalable, wide-spread deployment of biometric authentication are examined, and a cryptosystem capable of using fingerprint data as its key is presented.
Abstract: In this paper, the fundamental insecurities hampering a scalable, wide-spread deployment of biometric authentication are examined, and a cryptosystem capable of using fingerprint data as its key is presented. For our application, we focus on situations where a private key stored on a smartcard is used for authentication in a networked environment, and we assume an attacker can launch o -line attacks against a stolen card.Juels and Sudan's fuzzy vault is used as a starting point for building and analyzing a secure authentication scheme using fingerprints and smartcards called a figerprint vault. Fingerprint minutiae coordinates mi are encoded as elements in a nite eld F and the secret key is encoded in a polynomial f(x) over F[x]. The polynomial is evaluated at the minutiae locations, and the pairs (mi, f(mi)) are stored along with random (ci, di) cha points such that di ≠ f(ci). Given a matching fingerprint, a valid user can seperate out enough true points from the cha points to reconstruct f(x), and hence the original secret key.The parameters of the vault are selected such that the attacker's vault unlocking complexity is maximized, subject to zero unlocking complexity with a matching fingerprint and a reasonable amount of error. For a feature location measurement variance of 9 pixels, the optimal vault is 269 times more difficult to unlock for an attacker compared to a user posessing a matching fingerprint, along with approximately a 30% chance of unlocking failure.

472 citations

Proceedings ArticleDOI
25 Aug 1996
TL;DR: An improved minutia extraction algorithm that is much faster and more accurate than an earlier algorithm has been implemented and an alignment-based elastic matching algorithms has been developed for minutian matching.
Abstract: We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracy.

433 citations

Book ChapterDOI
TL;DR: This paper explores the realization of a previously proposed cryptographic construct, called fuzzy vault, with the fingerprint minutiae data, which aims to secure critical data with the fingerprints in a way that only the authorized user can access the secret by providing the valid fingerprint.
Abstract: Biometrics-based user authentication has several advantages over traditional password-based systems for standalone authentication applications, such as secure cellular phone access. This is also true for new authentication architectures known as crypto-biometric systems, where cryptography and biometrics are merged to achieve high security and user convenience at the same time. In this paper, we explore the realization of a previously proposed cryptographic construct, called fuzzy vault, with the fingerprint minutiae data. This construct aims to secure critical data (e.g., secret encryption key) with the fingerprint data in a way that only the authorized user can access the secret by providing the valid fingerprint. The results show that 128-bit AES keys can be secured with fingerprint minutiae data using the proposed system.

397 citations

Journal ArticleDOI
TL;DR: This work proposes to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion of experts for taking a final decision on identity authentication.
Abstract: Biometric person identity authentication is gaining more and more attention. The authentication task performed by an expert is a binary classification problem: reject or accept identity claim. Combining experts, each based on a different modality (speech, face, fingerprint, etc.), increases the performance and robustness of identity authentication systems. In this context, a key issue is the fusion of the different experts for taking a final decision (i.e., accept or reject identity claim). We propose to evaluate different binary classification schemes (support vector machine, multilayer perceptron, C4.5 decision tree, Fisher's linear discriminant, Bayesian classifier) to carry on the fusion. The experimental results show that support vector machines and Bayesian classifier achieve almost the same performances, and both outperform the other evaluated classifiers.

383 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023138
2022296
2021358
2020626
2019833