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Enregistrement W6995657895

Personal identification by the iris of the eye

2012· dissertation· en· W6995657895 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Notice bibliographique

RevueElectronic Sumy State University Institutional Repository (Sumy State University) · 2012
Typedissertation
Langueen
DomaineComputer Science
ThématiqueBiometric Identification and Security
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIris recognitionBiometricsIdentification (biology)IRIS (biosensor)Pattern recognition (psychology)Image processing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Imagine being able to go to an ATM to withdraw money without the need for a card or a password.You simply look into an ATM camera, which detects the pattern of the specks on your iris and releases funds from your account.The convenience of this technology is not limited to your banking transactions.Proponents of the technology predict that iris recognition systems will soon become popular for use at work, home, and for retail and online purchases.The core algorithms that underlie iris recognition were developed in the 1990's by Professor John Daugman, Ph.D.These were licensed to many developers of commercial iris cameras and systems including LG Electronics, Oki, Panasonic, Sagem, IrisGuard, and Sarnoff Labs.As of 2008, Daugman's algorithms are the basis of all commercially deployed iris recognition systems, although many alternative approaches have been studied and compared in the academic literature in hundreds of publications.Iris recognition remains a very active research topic in computing, engineering, statistics, and applied mathematics.Iris recognition is an automated method of biometric identification that uses mathematical patternrecognition techniques on video images of the irides of an individual's eyes, whose complex random patterns are unique and can be seen from some distance.Iris recognition uses camera technology with subtle infrared illumination to acquire images of the detailrich, intricate structures of the iris.Digital templates encoded from these patterns by mathematical and statistical algorithms allow unambiguous positive identification of an individual.Databases of enrolled templates are searched by matcher engines at speeds measured in the millions of templates per second per (single-core) CPU, and with infinitesimally small False Match rates.This technology not only offers convenience, but also promises greater safety and security.Top airport security officials have recently recognized iris identifiers as an important tool for increasing airport security and for improving upon current immigration practices.The United States is now experimenting with technology which European banking institutions and airports have been using experimentally for over a decade with much success.Iris recognition is becoming increasingly attractive to American consumers.Historically, the U.S. market has been reluctant to accept any form of biometric technology due to the fear of identity thefts and out of concern for other privacy matters.Recent studies have shown, however, that iris identification systems are actually the least susceptible, of any biometric technologies, to violations of privacy and wrongful identification by authorities.Like other biometric devices, iris recognition systems act primarily as a screening tool to allow or deny access to a particular place, rather than as a law enforcement tool to track down suspected criminals, as are DNA and fingerprints.Iris identification systems, like many other less imposing biometric devices, are used to screen individuals who are trying to gain access to more highly secure places or accounts, not to scan the general public at random.British Airways and Virgin Atlantic Airways at Heathrow Airport in London are hoping to use the technology more for convenience and efficiency purposesto expedite the passport control process.As a trialrun, 2,000 American and Canadian passengers, who previously had their iris' scanned at the airport, are allowed to proceed to a special line in the passport control area of the airline terminal to have their identity quickly and accurately verified by an iris reading camera.The first time the camera scanned a passenger's iris, the image was converted into a code and stored in a database.When the passenger goes through customs, he/she stands approximately 14 inches from a camera, waits a few seconds as the system attempts to match the image of the passenger's iris with those stored on the server, and is either granted or denied passage through customs based on this assessment.Another important use of iris detection systems is in immigration security.The U.S. government and the INS are exploring various iris identification programs for use by border control facilities.Another system that will very likely become standard procedure for tracking immigrants is the use of a smart card, or ID card, like the ones used for airport security, where the immigrant's iris code, along with other biometric information, is stored on the card.This is a technology that also has incredible prospects in the terrorist-tracking industry.Many industry observers predict widespread use of cameras, scanners, and smart-card readers, especially at airports.Too much reliance on such devices could be hazardous to national security since, like all computerized systems, any biometric system is vulnerable to skilled hackers.In fact, according to the most recent National Institute of Justice Research Report on Entry-Control Technologies, retina or iris pattern scanners are considered the most accurate of all biometric devices.S. Zolotova, ELA

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,859
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0000,001
Science ouverte0,0030,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,193
Écart entre enseignants0,186 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle