Personal authentication using palm-print features

Han, Chin Chuan; Cheng, Hsu Liang; Lin, Chih Lung; Fan, Kuo-Chin

doi:10.1016/s0031-3203(02)00037-7

Cited by 414 publications

(214 citation statements)

References 11 publications

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“…The experimental results, obtained on a database of 237 people (1,820 hand images), show that it achieves a very high recognition rate (100 percent), and an identification accuracy expressed in terms of an equal error rate (EER) of 0.58 percent and a total error rate (TER) of 0.72 percent. Compared with the approaches based on features obtained from the palm described in [9], [20], [21], [22], [30], our system achieves better results expressed in terms of the total error rate or equal error rate. The total identification time per person (as explained in the Section 6.2) consists of a fixed time (0.544 seconds) and a matching time, which is 0.003 milliseconds per template in the client database (on a computer with 1177 SPECint_base2000 and 1154 SPECfp_base2000).…”

Section: Resultsmentioning

confidence: 89%

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A biometric identification system based on eigenpalm and eigenfinger features

Ribarić

Fratrić

2005

IEEE Trans. Pattern Anal. Machine Intell.

306

180

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Abstract-This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

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Section: Resultsmentioning

confidence: 89%

“…Palm printbased biometric systems normally use the following characteristics: prominent palm-line features, the end points of these lines, texture, global texture energy, or some combination of these characteristics [7], [8], [9], [20], [21], [22].…”

Section: Related Workmentioning

confidence: 99%

A biometric identification system based on eigenpalm and eigenfinger features

Ribarić

Fratrić

2005

IEEE Trans. Pattern Anal. Machine Intell.

306

180

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“…• Other biometric identiÿcation technologies include keystroke recognition, palm-print features [29] or ear recognition [11].…”

Section: Other Bissmentioning

confidence: 99%

Biometric identification systems

et al. 2003

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In this paper, we provide an overview of the fundamentals of biometric identiÿcation, together with a description of the main biometric technologies currently in use, all of them within a common reference framework. A comparison on di erent qualitative parameters of these technologies is also given, so that the reader may have a clear perspective of advantages and disadvantages of each. A section on multibiometrics describes the state of the art in making these systems work coordinately. Fusion at di erent conceptual levels is described. Finally, a section on commercial issues provides the reader a perspective of the main companies currently involved in this ÿeld. ?

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“…There have been a number of palm-print recognition systems presented in the literature that make use of linebased features, e.g., [9,10,19]. Typically, these systems use line detectors, such as the Sobel operator, which scan the palmprint image for points of high intensity gradients to extract the line-features.…”

Section: Phase Congruency Featuresmentioning

confidence: 99%

Phase congruency features for palm-print verification

Pavešić

2009

IET Signal Process.

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The existing palm-print verification schemes have demonstrated good verification performance when identity claims have to be verified based on palm-print images of adequate quality (e.g., acquired in controlled illumination conditions, free from distortions caused by the pressure applied to the surface of the scanner, etc.). However, most of these schemes struggle with their verification performance when features have to be extracted from palm-print images of a poorer quality. In this paper we present a novel palm-print feature-extraction approach which deals with the presented problem by employing the 2D phase congruency model for line-feature-extraction. The proposed approach first computes a set of phase congruency features from a palm-print image and subsequently performs linear discriminant analysis on the computed features to represent them in a more compact manner. The approach was tested on two contrasting databases, namely, on the FE-LUKS and on the PolyU database. Encouraging results were achieved on both databases.

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Personal authentication using palm-print features

Abstract: Biometrics-based authentication is a verification ap-

Cited by 414 publications

References 11 publications

A biometric identification system based on eigenpalm and eigenfinger features

A biometric identification system based on eigenpalm and eigenfinger features

Biometric identification systems

Phase congruency features for palm-print verification

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