Fingerprint Identification Technology

Allen, Robert; Sankar, Pat; Prabhakar, Salil

doi:10.1007/1-84628-064-8_2

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…Consequently, ‘verifying’ or ‘authenticating’ identity of authorised ‘users’ was considered a primary concept in the field. With the expansion into law enforcement applications in 1970–1990 time period [13], the concept of ‘identification’ became relevant and law enforcement terms were added to the vocabulary of automated human recognition technologies. For example, automated technologies were used to connect a fingerprint ‘lifted’ from a crime scene to a ‘file’ print of a known individual.…”

Section: Historical Contextmentioning

confidence: 99%

Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37

Wayman

McIver

Waggett³

et al. 2014

IET biom.

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This study discusses a 10-year effort by Standards Committee 37 of the International Organisation for Standardisation/ International Electrotechnical Commission Joint Technical Committee 1 (ISO/IEC JTC1 SC37) to create a systematic vocabulary for the field of 'biometrics' based on international standards for vocabulary development. That process has now produced a new International Standard (ISO/IEC 2382-37:2012), which conceptualises and defines 121 terms that are most central to the proposed field. This study will review some of the philosophical and operational principles of vocabulary development within SC37, present 11 of the most commonly used standardised terms with their definitions and discuss some of the conceptual changes implicit in the new vocabulary.

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Section: Historical Contextmentioning

confidence: 99%

Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37

Wayman

McIver

Waggett³

et al. 2014

IET biom.

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“…The first approaches translate the manual pattern classification into computer-friendly codes based on the shape of the papillary ridge flow; this shape is named general pattern and classified as first-level detail (Figure 3). This operation resulted in a potential elimination of the manual search and filing errors, but did not offset the original pattern type assignment errors [7]. Later, the fingerprint pattern classification was automated using the ridge direction extracted from the fingerprint images.…”

Section: Developmentmentioning

confidence: 99%

Friction Ridge Skin—Automated Fingerprint Identification System (AFIS)

Meuwly

2013

Wiley Encyclopedia of Forensic Science

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This contribution describes the development and the forensic use of automated fingerprint identification systems ( AFISs ). AFISs were initially developed in order to overcome the limitations of the paper‐based fingerprint collections, by digitizing the ten‐print cards in computerized databases and to translate the manual pattern classification into computer‐friendly codes. Then, technologies to automate the fingerprint feature extraction and comparison were developed, and AFISs were implemented on a large scale in order to improve the process of identification of repetitive offenders based on the ten‐print cards. Further development of the fingerprint biometric technology allowed for the inclusion of palmprint reference databases and for the processing of fingermarks and palmmarks with, as a result, the partial automation of the forensic investigation and intelligence process. In the field of AFIS , the challenges for the future call for further automation of the feature extraction from low‐quality fingerprint and fingermark images, for more transparency in the processes, for the improvement of the interoperability of the systems on a global level and the combination of biometric modalities as well as for the use of fingerprint biometric technology and scientific methodology, to further develop the forensic friction ridge evaluation process.

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“…Atualmente o processo de capturaé dividido de acordo com o tipo de tecnologia: com ou sem toque do dedo (i. e. touchless) do usuário no dispositivo. Se por um lado, sistemas em que o toque no sensor se faz necessário, já alcançaram um nível de maturidade, por outro, sistemas que utilizam a tecnologia sem toque no sensor ainda estão nos seus primórdios, permitindo ainda diversos avanços no processo de identificação biométrica, a partir da impressão digital.2.1 Impressões digitais com toqueAtualmente diversas aplicações nasáreas forenses, civis e comerciais[1] fazem uso de impressões digitais. Nestes sistemas, a autenticação a partir do uso de imagens digitais das impressões digitais, permitem uma descrição real dos dedos, aumentando a confiabilidade do sistema.…”

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Projeto e implementação de uma rede neural artificial para detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque

Zaghetto

Aguiar

Zaghetto

et al. 2015

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations.

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Fingerprint Identification Technology

Cited by 17 publications

References 15 publications

Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37

Vocabulary harmonisation for biometrics: the development of ISO/IEC 2382 Part 37

Friction Ridge Skin—Automated Fingerprint Identification System (AFIS)

Projeto e implementação de uma rede neural artificial para detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque

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