Iris Card Scanner Software ((BETTER)) Free Download

Iris ID has been producing commercial iris recognition systems since 1997. In thousands of locations, IrisAccess authenticates the iris identity of more persons than all other iris platforms combined. Iris ID's rich experience in iris recognition is exemplified in the iCAM TD100.

The iCAM TD100 includes an optical system specifically designed and optimized to operate in perfect unison with the integrated high speed multi-sensor iris imager array. The iCAM TD100 automatically processes and outputs high quality ISO standards compliant iris images of a subject in less than one second as the device or the subject approaches the optimum capture distance.

Iris Card Scanner Software Free Download

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The Iris ID iCAM TD100 is compatible with several different software development kits and applications. Below is the list of development kits and products that are currently known to support this scanner. Please note that some applications and development kits require specific versions of the drivers to be installed. Please contact us if you need additional information.

Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance. The discriminating powers of all biometric technologies depend on the amount of entropy[1] they are able to encode and use in matching. Iris recognition is exceptional in this regard, enabling the avoidance of "collisions" (False Matches) even in cross-comparisons across massive populations.[2] Its major limitation is that image acquisition from distances greater than a meter or two, or without cooperation, can be very difficult. However, the technology is in development and iris recognition can be accomplished from even up to 10 meters away or in a live camera feed.[3]

Retinal scanning is a different, ocular-based biometric technology that uses the unique patterns on a person's retina blood vessels and is often confused with iris recognition. Iris recognition uses video camera technology with subtle near infrared illumination to acquire images of the detail-rich, intricate structures of the iris which are visible externally. Digital templates encoded from these patterns by mathematical and statistical algorithms allow the identification of an individual or someone pretending to be that individual.[4] 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 remarkably low false match rates.

At least 1.5 billion persons around the world (including 1.29 billion citizens of India, in the UIDAI / Aadhaar programme as updated on 30 November) have been enrolled in iris recognition systems for national ID, e-government services, benefits distribution, security, and convenience purposes such as passport-free automated border-crossings.[5][6][7][8][9][10][11][12] A key advantage of iris recognition, besides its speed of matching and its extreme resistance to false matches, is the stability[13] of the iris as an internal and protected, yet externally visible organ of the eye.

Although John Daugman developed and in the 1990s patented the first actual algorithms to perform iris recognition, published the first papers about it and gave the first live demonstrations, the concept behind this invention has a much longer history and today it benefits from many other active scientific contributors. In a 1953 clinical textbook, F.H. Adler[14] wrote: "In fact, the markings of the iris are so distinctive that it has been proposed to use photographs as a means of identification, instead of fingerprints." Adler referred to comments by the British ophthalmologist J.H. Doggart,[15] who in 1949 had written that: "Just as every human being has different fingerprints, so does the minute architecture of the iris exhibit variations in every subject examined. [Its features] represent a series of variable factors whose conceivable permutations and combinations are almost infinite." Later in the 1980s, two American ophthalmologists, L. Flom and Aran Safir managed to patent Adler's and Doggart's conjecture that the iris could serve as a human identifier, but they had no actual algorithm or implementation to perform it and so their patent remained conjecture. The roots of this conjecture stretch back even further: in 1892 the Frenchman A. Bertillon had documented nuances in "Tableau de l'iris humain". Divination of all sorts of things based on iris patterns goes back to ancient Egypt, to Chaldea in Babylonia, and to ancient Greece, as documented in stone inscriptions, painted ceramic artefacts, and the writings of Hippocrates. (Iris divination persists today, as "iridology.")[citation needed]

The core theoretical idea in Daugman's algorithms is that the failure of a test of statistical independence can be a very strong basis for pattern recognition, if there is sufficiently high entropy (enough degrees-of-freedom of random variation) among samples from different classes. In 1994 he patented this basis for iris recognition and its underlying computer vision algorithms for image processing, feature extraction, and matching, and published them in a paper.[16] These algorithms became widely licensed through a series of companies: IriScan (a start-up founded by Flom, Safir, and Daugman), Iridian, Sarnoff, Sensar, LG-Iris, Panasonic, Oki, BI2, IrisGuard, Unisys, Sagem, Enschede, Securimetrics and L-1, now owned by French company Morpho.

With various improvements over the years, these algorithms remain today the basis of all significant public deployments of iris recognition, and they are consistently top performers in NIST tests (implementations submitted by L-1, MorphoTrust and Morpho, for whom Daugman serves as Chief Scientist for Iris Recognition). But research on many aspects of this technology and on alternative methods has exploded, and today there is a rapidly growing academic literature on optics, photonics, sensors, biology, genetics, ergonomics, interfaces, decision theory, coding, compression, protocol, security, mathematical and hardware aspects of this technology.

Most flagship deployments of these algorithms have been at airports, in lieu of passport presentation, and for security screening using watch-lists. In the early years of this century, major deployments began at Amsterdam's Schiphol Airport and at ten UK airport terminals allowing frequent travellers to present their iris instead of their passport, in a programme called IRIS: Iris Recognition Immigration System. Similar systems exist along the US / Canada border, and many others. In the United Arab Emirates, all 32 air, land, and seaports deploy these algorithms to screen all persons entering the UAE requiring a visa. Because a large watch-list compiled among GCC States is exhaustively searched each time, the number of iris cross-comparisons climbed to 62 trillion in 10 years. The Government of India has enrolled the iris codes (as well as fingerprints) of more than 1.2 billion citizens in the UIDAI (Unique Identification Authority of India) programme for national ID and fraud prevention in entitlements distribution.[5] In a different type of application, iris is one of three biometric identification technologies internationally standardised since 2006 by ICAO for use in e-passports (the other two are fingerprint and face recognition).[17]

First the system has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye. Further subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris. The set of pixels containing only the iris, normalized by a rubber-sheet model to compensate for pupil dilation or constriction, is then analyzed to extract a bit pattern encoding the information needed to compare two iris images.

In the case of Daugman's algorithms, a Gabor wavelet transform is used. The result is a set of complex numbers that carry local amplitude and phase information about the iris pattern. In Daugman's algorithms, most amplitude information is discarded, and the 2048 bits representing an iris pattern consist of phase information (complex sign bits of the Gabor wavelet projections). Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination or camera gain, and contributes to the long-term usability of the biometric template.

For identification (one-to-many template matching) or verification (one-to-one template matching),[21] a template created by imaging an iris is compared to stored templates in a database. If the Hamming distance is below the decision threshold, a positive identification has effectively been made because of the statistical extreme improbability that two different persons could agree by chance ("collide") in so many bits, given the high entropy of iris templates.

It is an internal organ that is well protected against damage and wear by a highly transparent and sensitive membrane (the cornea). This distinguishes it from fingerprints, which can be difficult to recognize after years of certain types of manual labor. The iris is mostly flat, and its geometric configuration is only controlled by two complementary muscles (the sphincter pupillae and dilator pupillae) that control the diameter of the pupil. This makes the iris shape far more predictable than, for instance, that of the face.

Mathematically, iris recognition based upon the original Daugman patents or other similar or related patents define the strongest biometric in the world. Iris recognition will uniquely identify anyone, and easily discerns between identical twins. If a human can verify the process by which the iris images are obtained (at a customs station, entering or even walking by an embassy, as a desktop 2nd factor for authentication, etc.) or through the use of live eye detection (which varies lighting to trigger slight dilation of the pupil and variations across a quick scan which may take several image snapshots) then the integrity of the identification are extremely high. 2351a5e196