Fingerprint identification system operates by acquiring a fingerprint image and comparing it with the template image, therefore the accuracy of system depends highly on the quality of template image. ...In this paper, we propose a method to select template based on fingerprint image quality. In the proposed method, four features firstly are extracted from a fingerprint image. And then, a support vector machine (SVM) model is trained to analyze image qualities, and lastly the fingerprint image with the best quality is chosen as the template to match for each finger. We design an experiment to compare the proposed method with two common methods in FVC2000db1, FVC2000db2 and FVC2000db3. The results of experiment show us that the proposed method is more effective to improve the identification accuracy than the other two methods.
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 user's ability to interact with the sensor, further reducing fingerprint quality. Because quality of fingerprints varies according to the user population's 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.
The fingerprint quality can be used as a good predictor for fingerprint recognition performance. Knowing the fingerprint quality in advance is useful to improve the performance of fingerprint ...recognition system. In this paper, we propose an effective quality estimation system with 4 rules which are applied in two-step. Each rule consists of a Back-Propagation Neural Networks (BPNN) classifier based on Optimized Orientation Certainty Level (OOCL) features extracted locally from fingerprint images. Experimental results show that the proposed two-step OOCL method can estimate fingerprint quality more effectively.
Analyzing the quality of fingerprints in advance can be benefit for a fingerprint recognition system to improve its performance. Representative features for the quality assessment of fingerprint ...images from two existed types of capture devices are different. Orientation certainty level (OCL) is an effective method to extract image orientation feature. However it is not an effective estimation system to cooperate with the extracted features. In this paper, we explore the application of optimization theory, and support vector machine (SVM) in the field of image processing. Our proposed optimal orientation certainty level (OOCL) approach calculates the OCL for each block, extracts features from the optimal OCL system and uses the SVM classifier to determine whether an image should be accepted as an input to the recognition system. Experimental results show that the proposed OOCL method can improve the recognition rate than OCL method.
