Biometric identification through hand geometry measurements

Sánchez-Reillo, Raúl; Ávila, Carmen Sánchez; González‐Marcos, Ana

doi:10.1109/34.879796

Cited by 423 publications

(219 citation statements)

References 6 publications

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“…The signature analysis on the hand-shape boundary image is used to extract the image reference points, i.e., four fingertips, four interfinger points, and hand base, in a similar manner as detailed in [30]. The thumb features were not computed due to their poor reliability, as in [32], and its poor stability can be largely attributed to the peg-free imaging setup. All the feature distances are computed in terms of number of pixels in the binary image.…”

Section: B Hand-shape Featuresmentioning

confidence: 99%

Personal recognition using hand shape and texture

Kumar

Zhang

2006

IEEE Trans. on Image Process.

241

135

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Abstract-This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees ( 4 5, LMT), -NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.

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Section: B Hand-shape Featuresmentioning

confidence: 99%

Personal recognition using hand shape and texture

Kumar

Zhang

2006

IEEE Trans. on Image Process.

241

135

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“…The physical characteristics of an individual that can be used in biometric identification/verification systems are fingerprint [3], [4], hand geometry [5], [6], palm print [7], [8], [9], face [4], [10], iris [11], [12], retina [13], and ear [14]; the behavioral characteristics are signature [2], lip movement [15], speech [16], keystroke dynamics, gesture, and gait [1], [2]. Biometric systems based on a single biometric characteristic are referred to as unimodal systems.…”

mentioning

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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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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“…In early works, hand segmentation received little attention, given the fact that isolating hand from background, given a monochromatic or apriori known background was straightforward [1], [2].…”

Section: State Of the Artmentioning

confidence: 99%

Gaussian multiscale aggregation oriented to hand biometric segmentation in mobile devices

Sierra¹,

Ávila²,

Pozo³

et al. 2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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Abstract-New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile.However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation.For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task.Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments.The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

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Biometric identification through hand geometry measurements

Cited by 423 publications

References 6 publications

Personal recognition using hand shape and texture

Personal recognition using hand shape and texture

A biometric identification system based on eigenpalm and eigenfinger features

Gaussian multiscale aggregation oriented to hand biometric segmentation in mobile devices

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