Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2082
Title: Hybrid minutiae and edge corners feature points for increased fingerprint recognition performance
Authors: Nachar, Rabih 
Inaty, Elie 
Bonnin, Patrick J
Alayli, Yasser
Affiliations: Department of Telecommunications and Networking Engineering 
Department of Electrical Engineering 
Issue Date: 2020
Part of: Journal of pattern analysis and applications
Volume: 23
Issue: 1
Start page: 213
End page: 224
Abstract: 
In general, most fingerprint recognition systems are based on the minutiae feature points. When matching two fingerprint images, the goal in most recognition systems is to find the optimal transformation model that aligns their feature points in order to find among them the number of matched or aligned points and then generate a matching score. A major problem in feature extraction stage is that when the fingerprint image is of a poor quality due to skin conditions and sensor noise, that leads to many broken ridges in the image caused by cutline. In this case, the extraction of minutiae leads to a lot of spurious points and the performance of the system will degrade. Usually, image enhancement techniques are applied as preprocessing step to overcome this problem. In this work, we propose to use corner points on fingerprint ridges as new features in addition to the ridges minutiae in order to improve the recognition performance. Every ridge is decomposed into several straight edges (SEs). A straight edge is defined as a straight link of ridge points. On a ridge, the head of the first straight edge and the tail of the last one are two minutia and the intersections of the SEs are the ridge corners. Thus, we propose to use a ridge as primitive rather than individual points for matching. This primitive is a structure consisting of groups of both feature points which are minutiae and corners belonging to the same ridge. Based on this primitive, an intelligent matching technique is introduced using sets of feature points on the same primitive. As a result, the recognition performance is increased since it is based on ridge primitive matching rather than individual minutiae matching. Finally, our experimental results compared with those obtained by other well-known techniques in the literature demonstrate the effectiveness and efficiency of our proposed algorithm. Bitte loggen.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2082
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Telecommunications and Networking Engineering

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