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Personal Recognition Using Finger Knuckle Shape Oriented Features and Texture Analysis

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Usha, K. (Author)
مؤلفين آخرين: Ezhilarasan, M. (Co-Author)
المجلد/العدد: مج28, ع4
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2016
الصفحات: 416 - 431
DOI: 10.33948/0584-028-004-005
ISSN: 1319-1578
رقم MD: 974006
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Finger Knuckle Print | Angular Geometric Analysis Method | Curvelet Transform | Curvelet Knuckle | Principle Component Analysis | Hybrid Rule
رابط المحتوى:
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المستخلص: Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape-oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands, which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

ISSN: 1319-1578

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