Shimon K. Modi

Impact of Age Groups on Fingerprint Recognition Performance

Ever since introduction of automated fingerprint recognition in law enforcement in the 1970s it has been utilized in applications ranging from personal authentication to civilian border control. The increasing use of automated fingerprint recognition puts on it a challenge of processing a diverse range of fingerprints. The quality control module is important to this process because it supports consistent fingerprint detail extraction which helps in identification / verification. Inherent feature issues, such as poor ridge flow, and interaction issues, such as inconsistent finger placement, have an impact on captured fingerprint quality, which eventually affects overall system performance. Aging results in loss of collagen; compared to younger skin, aging skin is loose and dry. Decreased skin firmness directly affects the quality of fingerprints acquired by sensors. Medical conditions such as arthritis may affect the users ability to interact with the sensor, further reducing fingerprint quality. Because quality of fingerprints varies according to the user populations ages and fingerprint quality has an impact on overall system performance, it is important to understand the significance of fingerprint samples from different age groups. This research examines the effects of fingerprints from different age groups on quality levels, minutiae count, and performance of a minutiae-based matcher. The results show a difference in fingerprint image quality across age groups, most pronounced in the 62-and-older age group, confirming the work of [7].

Keystroke Dynamics Verification Using a Spontaneously Generated Password

Current keystroke dynamics applications have tackled the problem of traditional knowledge-based static password verification, but the problem of spontaneous password verification persists. The intent of this study was to examine the predictive strength of typing patterns for spontaneous passwords. The typing patterns of an individual typing at a DELLreg keyboard on a DELL OptiPlexreg GX260 machine were recorded. Variables collected included keystroke press time and keystroke latency. Computed performance measures included false match rates (FMR) and false non match rates (FNMR) at various threshold levels

Investigating the relationship between fingerprint image quality and skin characteristics

This paper reports the correlations between skin characteristics, such as moisture, oiliness, elasticity, and temperature of the skin, and fingerprint image quality across three sensing technologies. Fingerprint images from the index finger of the dominant hand of 190 individuals, were collected on nine different fingerprint sensors. The sensors included four capacitance sensors, four optical sensors and one thermal fingerprint sensor. Skin characteristics included temperature, moisture, oiliness and elasticity, were measured prior to the initial interaction with each of the individual sensors. The analysis of the full dataset indicated that the sensing technology and interaction type (swipe or touch) were moderately and weakly correlated respectively with image quality scores. Correlation analysis between image quality scores and the skin characteristics were also made on subsets of data, divided by the sensing technology. The results did not identify any significant correlations. This indicates that further work is necessary to determine the type of relationship between the variables, and how they impact image quality and matching performance.

Statistical analysis of fingerprint sensor interoperability performance

The proliferation of networked authentication systems has put focus on the issue of interoperability. Fingerprint sensors are based on a variety of different technologies that introduce inconsistent distortions and variations in the feature set of the captured image, which makes the goal of interoperability challenging. The motivation of this research was to examine the effect of fingerprint sensor interoperability on the performance of a minutiae based matcher. A statistical analysis framework for testing interoperability was formulated to test similarity of minutiae count, image quality and similarity of performance between native and interoperable datasets. False non-match rate (FNMR) was used as the performance metric in this research. Interoperability performance analysis was conducted on each sensor dataset and also by grouping datasets based on the acquisition technology and interaction type of the acquisition sensor. The lowest interoperable FNMR observed was 0.12%.

Performance analysis for multi sensor fingerprint recognition system

The increasing use of distributed authentication architecture has made interoperability of systems an important issue. Interoperability of systems reflects the maturity of the technology and also improves confidence of users in the technology. Biometric systems are not immune to the concerns of interoperability. Interoperability of fingerprint sensors and its effect on the overall performance of the recognition system is an area of interest with a considerable amount of work directed towards it. This research analyzed effects of interoperability on error rates for fingerprint datasets captured from two optical sensors and a capacitive sensor when using a single commercially available fingerprint matching algorithm. The main aim of this research was to emulate a centralized storage and matching architecture with multiple acquisition stations. Fingerprints were collected from 44 individuals on all three sensors and interoperable False Reject Rates of less than .31% were achieved using two different enrolment strategies.

Fingerprint recognition performance evaluation for mobile ID applications

According to a report by Frost and Sullivan in 2007, revenues for non-AFIS fingerprint devices in notebook PCs and wireless devices is anticipated to grow from

Image Quality and Minutiae Count Comparison for Genuine and Artificial Fingerprints

148.5 million to

Comparison of face image quality metrics: Electronic and legacy mug shots

1588.0 million by 2014, a compound annual growth rate of 40.3% [1]. The AFIS market has a compound annual growth rate of 15.2% with revenues of

Examination of fingerprint image quality and performance on force acquisition vis-à-vis auto-capture

445.0 million in 2007. With the development of mobile applications in a number of different market segments, such as healthcare, retail, and law enforcement, this paper analyzed the performance of fingerprints of different sizes, from different sensors (commercialy available optical and capacitance) which were generated in accordance with NIST Mobile ID best practices document using an automated image cropping process. The minutiae count, image quality scores, false non match rates (FNMR) and false match rates (FMR) were evaluated for images of seven different image sizes, ranging from 0.098?X0.126? to 0.578?X0.618?. The results provide insight into constraints of fingerprint image sizes, interoperability and fingerprint matching of different sizes in a mobile ID environment.

Securing the manufacturing environment using biometrics

The vulnerabilities of biometric sensors have been discussed extensively in the literature and popularized in films and television shows. This research examines the image quality of an artificial print as compared to a genuine finger, and examines the characteristics of the two, including minutiae counts and image quality, as repeated samples are taken.