the vascular pattern-based recognition stems based on liveness of a person arc more reliable and secure system as compare to conventional methods. this paper proposes a method for finger vein recognition using modifie...
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ISBN:
(纸本)9781728113807
the vascular pattern-based recognition stems based on liveness of a person arc more reliable and secure system as compare to conventional methods. this paper proposes a method for finger vein recognition using modified maximum edges Position to improve the performance in presence of uneven illumination. Unlike the available binary patterns, this feature collects the sign and magnitude of edge which capitalize the gradient difference in different directions. Highly gradient directions are identified by choosing maximum values of edges. the performances of the proposed show a significant improvement. From this Maximum Edge Position Octal pattern (MEPOP), the classification accuracy of the system is of 89.4654% and Equal Error Rate to 12. 4738%.
In this paper, we consider the general problem of technical document interpretation, applied to the documents of the French Telephonic Operator, France Telecom. More precisely, we focus the content of this paper on th...
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In this paper, a framework for fingerprint recognition is introduced. the framework contains several algorithms for fingerprint matching and feature extraction, as well as the evaluation protocol for several fingerpri...
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Although face recognition confronts several challenges, it has garnered considerable attention over the last two decades. A new proposal of local directional pattern (LDP) operator for face recognition has been introd...
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ISBN:
(纸本)9781665434027
Although face recognition confronts several challenges, it has garnered considerable attention over the last two decades. A new proposal of local directional pattern (LDP) operator for face recognition has been introduced to overcome different challenges confronting face recognition systems. this new proposal is known as Multi-Mask Local Directional pattern (MMLDP), which integrates the advantages of LDP operator depending on Robinson and Kirsch masks into one feature vector to give more discrimination information. Firstly, the LDP operator applies various masks to obtain the face image feature, and after that, methods such as Left-Right fusion, Right-Left fusion, Down-Up fusion, and Up-Down fusion are used to fuse the features. Finally, by employing a support vector machine (SVM), the classification of the feature ensues. the conducted experiments utilized the Yale database, and outcomes show that fusion of the features for various masks based on LDP improved performance withthe best classification accuracy of 94.2857% compared to LDP and other conventional approaches for face recognition.
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