This study investigated the effect of force levels (3, 5, 7, 9 and 11N) on fingerprint matching performance, image quality scores and minutiae count between optical and capacitance sensors. Three images were collected from the right index fingers of 75 participants for each sensing technology. Descriptive statistics analysis of variance and Kruskal-Wallis non-parametric tests were conducted to assess significant differences in minutiae counts and image quality scores, by force level. The results reveal a significant difference in image quality score by force level and sensor technology in contrast to minutiae count for the capacitance sensor. The image quality score is one of the many factors that influence the system matching performance, yet the removal of low quality images does not improve the system performance at each force level. Further research is needed to identify other manipulatable factors to improve the interaction between a user and device and the subsequent matching performance.
The purpose of this study was to investigate bacterial recovery and transfer from three biometric sensors and the survivability of bacteria on the devices. The modalities tested were fingerprint, hand geometry and hand vein recognition, all of which require sensor contact with the hand or fingers to collect the biometric. Each sensor was tested separately with two species of bacteria, Staphylococcus aureus and Escherichia coli.Survivability was investigated by sterilizing the sensor surface, applying a known volume of diluted bacterial culture to the sensor and allowing it to dry. Bacteria were recovered at 5, 20, 40 and 60 minutes after drying by touching the contaminated device with a sterile finger cot. The finger cot was re-suspended in 5 mL of saline solution, and plated dilutions to obtain live cells counts from the bacterial recovery. The transferability of bacteria from each device surface was investigated by touching the contaminated device and then touching a plate to transfer the bacteria to growth medium to obtain live cell counts. The time lapse between consecutive touches was one minute, with the number of touches was n = 50. Again, S. aureus and E. coli were used separately as detection organisms. This paper will descrbe the results of the study in terms of survival curves and transfer curves of each bacterial strain for each device.
-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.
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