An automated palmprint recognition system

Connie, Tee; Teoh, Andrew Beng Jin; Goh, Michael Kah Ong; Ling, David Ngo Chek

doi:10.1016/j.imavis.2005.01.002

Cited by 249 publications

(128 citation statements)

References 23 publications

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“…So there are only the owner ' s biometric features stored in the system. Therefore, we evaluate recognition accuracy of the system in terms of verification rate [14] of sumdifference ordinal code and total success rate [12], both obtained through one-to-one image matching. There are totally 800 intra-class comparisons and 3,160 inter-class comparisons.…”

Section: Performance Evaluation Of the Systemmentioning

confidence: 99%

“…In addition, it is the most accurate for identity verification. Total success rate (TSR) [12] is employed to evaluate recognition accuracy in a further step. It is calculated considering both correct accepted and correct rejected items:…”

Section: Performance Evaluation Of the Systemmentioning

confidence: 99%

“…As a result, they contains more discriminative features. Furthermore, there is less alignment error in images of the PolyU database [12] after preprocessing [3]. Thus, all four algorithms perform better.…”

Section: Verification Experiments On the Polyu Palmprint Databasementioning

confidence: 99%

“…Various palmprint recognition approaches have been proposed in the literatures [1][2][3][4][5][6][7][8][9][10][11][12]. Almost all available algorithms involve thousands of large-size convolutions or filtering, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Embedded Palmprint Recognition System on Mobile Devices

Han¹,

Tan²,

Sun³

et al.

Advances in Biometrics

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Abstract. There are increasing requirements for mobile personal identification, e.g. to protect identity theft in wireless applications. Based on built-in cameras of mobile devices, palmprint images may be captured and analyzed for individual authentication. However, current available palmprint recognition methods are not suitable for real-time implementations due to the limited computational resources of handheld devices, such as PDA or mobile phones. To solve this problem, in this paper, we propose a sum-difference ordinal filter to extract discriminative features of palmprint using only +/-operations on image intensities. It takes less than 200 ms for our algorithm to verify the identity of a palmprint image on a HP iPAQ PDA, about 1/10 of state-of-the-art methods ' complexity, while this approach also achieves high accuracy on the PolyU palmprint database. Thanks to the efficient palmprint feature encoding scheme, we develop a real-time embedded palmprint recognition system, working on the HP PDA.

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Section: Performance Evaluation Of the Systemmentioning

confidence: 99%

Section: Performance Evaluation Of the Systemmentioning

confidence: 99%

Section: Verification Experiments On the Polyu Palmprint Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Embedded Palmprint Recognition System on Mobile Devices

Han¹,

Tan²,

Sun³

et al.

Advances in Biometrics

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show abstract

“…Huang et al proposed a modified finite radon transformation to extract principal lines [12]. images in a multi-resolution-multi-frequency representation, they also adopted wavelet transformation at the same time [15]. Moreover, Hu et al employed 2D Locality Preserving Projections (2DLPP) for palmprint recognition [16].…”

Section: Introductionmentioning

confidence: 99%

Palmprint identification using restricted fusion

Jia¹,

Liu²,

Chau³

2008

Applied Mathematics and Computation

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Abstract. In this paper, we propose two palmprint identification schemes using fusion strategy. In the first fusion scheme, firstly, the principal lines of test image is extracted, and matched with that of all training images. Secondly, those training images with large matching scores are selected to construct a small training sub-database. At last, the decision level fusion, combing matching scores of principal lines and Locality Preserving Projections features, has been made for final identification in small training sub-database. From another point of view, it can be seen that the fusion is restricted by the previous results of principal lines matching, so we call it as restricted fusion. The second fusion scheme is similar to the first one. Just the fusion order is changed. The results of experiments conducted on PolyU palmprint database show that the proposed schemes can achieve 100% accurate recognition rate.

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A Palmprint Authentication System

Zhang

Kong

et al.

Handbook of Biometrics

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An automated palmprint recognition system

Cited by 249 publications

References 23 publications

Embedded Palmprint Recognition System on Mobile Devices

Embedded Palmprint Recognition System on Mobile Devices

Palmprint identification using restricted fusion

A Palmprint Authentication System

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