Research on preprocessing of palmprint image based on adaptive threshold and Euclidian distance

Qu, Zhong; Wang, Zhengyong

doi:10.1109/icnc.2010.5583664

Cited by 6 publications

(14 citation statements)

References 7 publications

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“…However, the percentage failure can be reduced by the improvement of the Otsu binarisation method to face the poor quality of some images. The improved variant of the Otsu method proposed in [20] or the minimum-error method [12] can be used to test our method.…”

Section: Discussionmentioning

confidence: 99%

“…Some methods employ geometric techniques [13,15,23,24]. They are based on key point's detection techniques, geometric calculation, detection of centre of gravity [13], or tracing of the curve representing the Euclidean distances between hand boundary points and a reference center point [20]. These methods provide a fixed ROI size.…”

Section: Prior Workmentioning

confidence: 99%

“…If we consider the methods proposed by [2,20], the ROI extraction seems successful. However, the use of PolyU database misleads the test results.…”

Section: Introductionmentioning

confidence: 97%

“…Therefore, this method must be extended to support other hand images without this limit. In the second extraction method [20], the authors proposed an enhanced ROI extraction which may cover totally the hand palm. However, the device used maintains a fixed size for all individuals.…”

Section: Introductionmentioning

confidence: 99%

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Extraction method of Region of Interest from hand palm: Application with contactless and touchable devices

Artabaz

Benatchba

Koudil

et al. 2014

2014 10th International Conference on Information Assurance and Security

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Palm print is one of the modalities that offer high recognition accuracy. The recognition process depends on an optimized ROI (Region of Interest) extraction. This extraction is affected by several factors including the device used and the acquisition conditions. The acquisition mode can alter some image properties like rotation, translation and scale. Some devices are designed to maintain hand in a fixed position and delimit a subspace of the hand. On the other hand, contactlessdevices offer more convenience and flexibility but lead to altered images. ROI extraction methods must consider the acquisition device (with contact or contactless). In this paper, we propose a ROI extraction method that addresses this issue.We test our method on two databases PolyU and CASIA which illustrate the impact of using contactless device unlike the PolyU device. Then, we test performances of the palmprint biometric system. We use a Fisher Linear Discriminant projection (FLD) to extract features from ROI transformed into the frequency domain. Our proposed method can significantly cover a great portion of the palm in the two databases.Performances obtained with the proposed palmprint system are promising.

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Section: Discussionmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

“…If we consider the methods proposed by [2,20], the ROI extraction seems successful. However, the use of PolyU database misleads the test results.…”

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extraction method of Region of Interest from hand palm: Application with contactless and touchable devices

Artabaz

Benatchba

Koudil

et al. 2014

2014 10th International Conference on Information Assurance and Security

View full text Add to dashboard Cite

show abstract

“… Find the Euclidean distance of all contour pixels with respect to the reference point and plot these all distance with respect to index of all points. The maxima in the plot represent the tip points and the minima represent the valley points of the palm [12]. Hand geometry is considered to achieve medium security, but with several advantages compared to other techniques:  Medium cost as it only needs a platform and medium resolution reader or camera.…”

Section: Geometrical Features and Region Of Interest (Roi) Extractionmentioning

confidence: 99%

Person Verification using Fusion of Palm Geometry and Palm Print

Chate¹,

Ubale²,

Joshi³

2013

IJCA

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This paper proposes a biometric person verification system based using hand images. This paper attempts to improve the performance of palm print-based verification system by integrating hand geometry features at different levels of fusion. In this work geometrical features such as finger lengths and finger widths are used. For extraction of the palm print features Gabor filter based approach is utilized. For matching, the newly acquired biometric samples are compared with those stored in the system database, at the enrolment stage. Unlike other bimodal biometric systems, the users do not have to undergo the inconvenience of using two different sensors since the palm print and hand geometry features can be acquired from the same image, using single capturing device at the same time. The capturing device has a resolution of 640X480 pixels. Our experimental results on the image dataset from 300 users confirm the utility of combining hand geometry features with those from palm prints using a simple image acquisition setup. It also illustrates the performance of different fusion methods such as decision level, score level. FAR of 0.28% and FRR of 2.0% is achieved for the database using score level fusion of palm geometry and palm print.

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