Xperienced Programmers' Expert Group

XPEG was conceptualized by few ISM (Indian School of Mines, Dhanbad) students in January-2002 with an idea of making less costly biometric system to be used at high security areas. In January-2003 XPEG Research and Development team  completed its theoretical processing on the problem and submitted a detailed report dealing the solution. XPEG programmers team started its work on based upon the solution provided by the R&D team. In July-2003 XPEG came up with a new unique biometric authentication system using some of the very advanced techniques on Face and Voice processing (developed by XPEG R&D team) that worked on the standalone system. Analyzing the present market needs and cost affairs XPEG started working on the feasibility of a network biometric system. In December-2003 XPEG finished working on the problem and came up with a biometric system that could work on a network environment using some new protocol suits developed by XPEG. This system was named as XSEC-2.1 (X Security Version-2.1).

XSEC-2.1 Overview

The main purpose of XSEC project was to develop a user authentication and identification system that could work upon biological features of the user delivering high precision to be used for different high security purposes. Click below to read more about the XSEC-2.1 system. This section has a detailed discussion upon the XSEC architecture, Market opportunities, system backbone etc.

Biometric Fundamentals

Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic. Among the features measured are; face, fingerprints, hand geometry, handwriting, iris, retinal, vein, and voice. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. As the level of security breaches and transaction fraud increases, the need for highly secure identification and personal verification technologies is becoming apparent. Biometric-based solutions are able to provide for confidential financial transactions and personal data privacy. The need for biometrics can be found in federal, state and local governments, in the military, and in commercial applications. Enterprise-wide network security infrastructures, government IDs, secure electronic banking, investing and other financial transactions, retail sales, law enforcement, and health and social services are already benefiting from these technologies.

Biometric Applications

Biometrics is a rapidly evolving technology which is being widely used in forensics such as criminal identification and prison security, and has the potential to be used in a large range of civilian application areas. Biometrics can be used to prevent unauthorized access to ATMs, cellular phones, smart cards, desktop PCs, workstations, and computer networks. It can be used during transactions conducted via telephone and internet (electronic commerce and electronic banking). In automobiles, biometrics can replace keys with key-less entry devices.

Miscellaneous Discussions

Biometric decision-making is frequently misunderstood. For the vast majority of technologies and systems, there is no such thing as a 100% match, though systems can provide a very high degree of certainty. The biometric decision-making process is comprised of various components, as indicated below.

Matching - The comparison of biometric templates to determine their degree of similarity or correlation. A match attempt results in a score that, in most systems, is compared against a threshold. If the score exceeds the threshold, the result is a match; if the score falls below the threshold, the result is a non-match. Biometric comparisons take place when proprietary algorithms process biometric templates. These algorithms manipulate the data contained in the template in order to make valid comparisons, accounting for variations in placement, background noise, etc.

The matching process involves the comparison of the match template, created upon sample submission, with the reference template(s) already on file. In 1:1 verification systems, there is generally a single match template matched against a reference template. In 1:N identification systems, the single match template can be matched against dozens, thousands, even millions of reference templates.

In most systems, reference and match templates should never be identical. An identical match is an indicator that some sort of fraud is taking place, such as the resubmission of an intercepted or otherwise compromised template.

Score – A number indicating the degree of similarity or correlation of a biometric match. Traditional verification methods – passwords, PINs, keys, and tokens - are binary, offering only a strict yes/no response. This is not the case with most biometric systems. Nearly all biometric systems are based on matching algorithms that generate a score subsequent to a match attempt. This score represents the degree of correlation between the match template and the reference template. There is no standard scale used for biometric scoring: for some vendors a scale of 1-100 might be used, others might use a scale of –1 to 1; some vendors may use a logarithmic scale and others a linear scale. Regardless of the scale employed, this verification score is compared to the system’s threshold to determine how successful a verification attempt has been.

Threshold - A predefined number, often controlled by a biometric system administrator, which establishes the degree of correlation necessary for a comparison to be deemed a match. If the score resulting from template comparison exceeds the threshold, the templates are a “match” (though the templates themselves are not identical). When a biometric system is set to low security, the threshold for a successful match is more forgiving than when a system is set to high security.

Decision – The result of the comparison between the score and the threshold. The decisions a biometric system can make include match, non-match, and inconclusive, although varying degrees of strong matches and non-matches are possible. Depending on the type of biometric system deployed, a match might grant access to resources, a non-match might limit access to resources, while inconclusive may prompt the user to provide another sample.

One of the most interesting facts about most biometric technologies is that unique biometric templates are generated every time a user interacts with a biometric system. As an example, two immediately successive placements of a finger on a biometric device generate entirely different templates. These templates, when processed by a vendor’s algorithm, are recognizable as being from the same person, but are not identical. In theory, a user could place the same finger on a biometric device for years and never generate an identical template.

Therefore, for most technologies, there is simply no such thing as a 100% match. This is not to imply that the systems are not secure – biometric systems may be able to verify identify with error rates of less than 1/100,000 or 1/1,000,000. However, claims of 100% accuracy are misleading and are not reflective of the technology’s basic operation.