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Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-02-07
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Biometrics based personal authentication has been found to be an effective method for recognizing, with high confidence, a person’s identity.
With the emergence of reliable and inexpensive 3D scanners, recent years have witnessed a growing interest in developing 3D biometrics systems.
As a commonsense, matching algorithms are crucial for such systems.
In this paper, we focus on investigating identification methods for two specific 3D biometric identifiers, 3D ear and 3D palmprint.
Specifically, we propose a Multi-Dictionary based Collaborative Representation (MDCR) framework for classification, which can reduce the negative effects aroused by some local regions.
With MDCR, a range map is partitioned into overlapping blocks and, from each block, a feature vector is extracted.
At the dictionary construction stage, feature vectors from blocks having the same locations in gallery samples can form a dictionary and, accordingly, multiple dictionaries are obtained.
Given a probe sample, by coding its each feature vector on the corresponding dictionary, multiple class labels can be obtained and then we use a simple majority-based voting scheme to make the final decision.
In addition, a novel patch-wise and statistics-based feature extraction scheme is proposed, combining the range image’s local surface type information and local dominant orientation information.
The effectiveness of the proposed approach has been corroborated by extensive experiments conducted on two large-scale and widely-used benchmark datasets, the UND Collection J2 3D ear dataset and the PolyU 3D palmprint dataset.
To make the results reproducible, we have publicly released the source code.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Lin& Zhu, Xining& Li, Lida. 2019. Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196762
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Lin…[et al.]. Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1196762
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Lin& Zhu, Xining& Li, Lida. Multi-Dictionary Based Collaborative Representation: With Applications to 3D Ear and 3D Palmprint Identification. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196762
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1196762
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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