Automatic Palmprint Identification based on High Order Zernike Moment

S.,

doi:10.3844/ajassp.2012.759.765

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Biometrics uses a variety of techniques for identifying a person based on the certain physiological or behavioral attributes. These attributes include fingerprint Jain et al (1997; facial features Sonkamble and Thool (2011); Liu and Wechsler (2001), retina and iris patterns Wildes (1997), speech patterns Chou (2000), hand geometry Sanchez-Reillo et al (2000) and palmprint Zhang and Shu (1999); Duta et al (2002); You et al (2002); Kong and Zhang (2002); Chen et al (2001); Gayathri and Ramamoorthy et al (2012a;2012b), Gayathri and Ramamoorthy et al (2012c); Krishneswari and Arumugam (2012) and Haralick (1979). Biometric features of human being have a unique excellence: It is very ambiguous to remember the lengthy passwords and PIN numbers but biometric passwords are readily available for quick reference for identification.…”

Section: Introductionmentioning

confidence: 99%

Palmprint Recognition using Feature Level Fusion

Gayathri¹,

Ramamoorthy²

2012

Journal of Computer Science

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Problem statement: Palmprint based biometric method has gained high impact over the other biometric methods due to its ease of acquisition, reliability and high client acceptance. Multiple feature extraction from image gives higher accuracy of the authentication system. Approach: This study presents the palmprint based identification methodology which uses the Gabor wavelet entropy to extract multiple features existing on the palm print, by using a feature level fusion using Dempster-Shafer theory and are classified using nearest neighbor approach. A feature having the same vector can be grouped together using wavelet transform. A different feature of image using wavelet can be extracted. Some of the features that can be extracted using wavelet entropy consist of contrast, correlation, energy and homogeneity. The features are fused at feature levels. Palmprint matching is then performed by using the nearest neighbor classifier. Results and Conclusion: We selected 100 individualsâ left hand palm images; every person is 6 and the total is 600. Later we got every person each palm image as a template (total 100). The remaining 500 were treated as the training samples. The experimental results achieve recognition accuracy of 98.6% and interesting working point with False Acceptance Rate (FAR) of = 0.03% and False Rejection Rate (FRR) of = 1.4% on the publicly available database of The Hong Kong Polytechnic University. Experimental assessment using palmprint image databases clearly validates the efficient recognition performance of the suggested algorithm compared with the conventional palmprint recognition algorithms

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Section: Introductionmentioning

confidence: 99%

Palmprint Recognition using Feature Level Fusion

Gayathri¹,

Ramamoorthy²

2012

Journal of Computer Science

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“…Palmprint recognition is an efficient biometric solution for authentication system. The existence of several hand-based authentication commercial systems indicates the effectiveness of this type of biometric (Gayathri and Ramamoorthy, 2012b).…”

Section: Introductionmentioning

confidence: 99%

Palmprint Verification Using Invariant Moments Based on Wavelet Transform

Hussein

Nordin

2014

Journal of Computer Science

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Data security is one of the important issues among computer users. Data security can prevent fraudulent users from accessing an individual's personal data. The biometrics recognition as one of the most important parts in the security of the data and the application of computer vision. The biometrics is the authentication method used in a wide variety of applications such as e-banking, e-commerce, e-government and many others. A biometric system is one which requires the recognition of a pattern, whereby it enables the differentiation of features from one individual to another. Biometric technologies, thus may be defined as the automated methods of identifying, or authenticating, the identity of a living person based on physiological or behavioral traits. This study emphasizes palmprint recognition, which provides a wide deployment range of authentication methods. The palmprint contains principal lines, wrinkles, fine lines, ridges and surface area; thus the palmprint of person differs from one to another. Previous researchers have difficulty extracting the features of a palm print, because of the effects of rotation, translation and scaling changes and the accuracy rate of verification performance needs to be improved. The aim of this study is to extract shape features using an invariant moments algorithm based on wavelet transform and identify the person's verification. This model has shown a promising results without the effects of rotation, translation and scaling of objects, because it is associated with the use of a good description of shape features. This system has been tested using databases from the Indian Institute of Technology, Kanpur (IITK), by using the False Rejection Rate (FRR) and False Acceptance Rate (FAR), we may calculate the accuracy rate of verification. The experiment shows a 97.99% accuracy rate of verification.

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2D and 3D Image Analysis by Moments

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Automatic Palmprint Identification based on High Order Zernike Moment

Cited by 12 publications

References 15 publications

Palmprint Recognition using Feature Level Fusion

Palmprint Recognition using Feature Level Fusion

Palmprint Verification Using Invariant Moments Based on Wavelet Transform

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