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Additional info for Modelling and Implementation of Complex Systems: Proceedings of the 4th International Symposium, MISC 2016, Constantine, Algeria, May 7-8, 2016, Constantine, Algeria
In low resolution images features such as principal lines, wrinkles and textures can be extracted with less than 100 dpi (dots per inch) . However, features like minutiae points, ridges and singular points can be obtained from high resolution images with at least 400 dpi . In general, high resolution images are suitable for forensic applications such as criminal detection, while low resolution images essential for civil and commercial applications such as access control. Initially palmprint research focused on high-resolution images but now almost all research is on low resolution images for civil and commercial applications [4–6].
This degree of similarity is called local quality score LQS. To compute the LQS, we propose to calculate the similarity between the gray level histogram hT of the test block and the gray level histogram of the reference block hR using the following formula: 255 LQS = dðhT , hR Þ = ∑ min½hT ðiÞ, hR ðiÞ ð2Þ i=0 where hT(i) and hR(i) are the values of the test and reference block histograms at the gray level i. 255 The LQS takes its value in the interval [0, L] where L = ∑ hR ðiÞ, 0 means a poor i=0 quality block, while L means good quality block.
The biometric image acquired from this step may undergo a number of distortions such as poor illumination on a face, cuts on a ﬁngerprint and reﬂections in an iris. This may signiﬁcantly decrease the performances of the biometric systems. In order to face up to this problem, researchers have recently deﬁned the concept of the biometric image quality measurement. It is useful in different settings (enrolment, identiﬁcation and veriﬁcation) as well as in different processing phases (pre-processing, matching and decision) of a biometric system .