Ever since introduction of automated fingerprint foreground centroid are given more weight. A ratio of the recognition in law enforcement in the 1970s it has been utilized in weighted sum of dominant-direction blocks and the weighted applications ranging from personal authentication to civilian sum of foreground blocks is used to compute image quality border control. The increasing use of automated fingerprint ([3]).[4] perform image quality assessment using the recognition puts on it a challenge of processing a diverse range of fingerprint's global structure: a 2D Discrete Fourier Transform fingerprints. The quality control module is important to this process because it supports consistent fingerprint detail is calculated, and the measure of energy concentration hi extraction which helps in identification / verification. Inherent regions of interest is used as a determinant of quality; higher feature issues, such as poor ridge flow, and interaction issues, energy concentrations yield better image quality. Tabassi and such as inconsistent finger placement, have an impact on Wilson describe an approach to classifying image quality captured fingerprint quality, which eventually affects overall assessment whereby the quality of fingerprint features used for system performance. Aging results in loss of collagen; compared matching operations is computed and defines the degree of to younger skin, aging skin is loose and dry. Decreased skin separation between match and non-match distributions ([6]). firmness directly affects the quality of fingerprints acquired by In their work, a neural network is trained to map this degree of sensors. Medical conditions such as arthritis may affect the user's separation according to levels of quality, thus making ability to interact with the sensor, further reducing fingerprint . . . . . o quality. Because quality of fingerprints varies according to the fingerprat image quality an idicator of matcher performance. user population's ages and fingerprint quality has an impact on The impact of image quality degradation on performance of overall system performance, it is important to understand the fingerprint matching systems shows that ridge-based matchers significance of fingerprint samples from different age groups.outperform minutiae-based matchers on lesser-quality images This research examines the effects of fingerprints from different ([1]). This also holds true when examining different age groups on quality levels, minutiae count, and performance of populations; in one such case, fingerprint image quality for a minutiae-based matcher. The results show a difference in two groups (18-25, >62) are significantly different ([7]). fingerprint image quality across age groups, most pronounced in Further there was a negative impact on fingerprint algorithm the 62-and-older age group, confirming the work of 171.p hef e tse t n ae gops (] Ther goalgofithi performance on these two age groups ([5]). The goal of this Keywords-fingerprint quality; fingerprint performance; impact paper is to e...

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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.