New algorithm of extraction of palmprint region of interest (ROI)

Harun, Nurzalina; Rahman, Wan Eny Zarina Wan Abd; Abidin, Siti Zaleha Zainal; Othman, Puwira Jaya

doi:10.1088/1742-6596/890/1/012024

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The ROI extraction phase plays an important role in palmprint verification, since the accuracy of the whole process is highly dependent on ROI extraction, where several findings have been discussed in previous work [14][15][16][17][18][19][20][21][22][23][24][25]. In [18], ROI extraction steps consist of (i) skin-color thresholding, (ii) hand valley detection, and (iii) finding palm region.…”

Section: Roi Extractionmentioning

confidence: 99%

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

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Section: Roi Extractionmentioning

confidence: 99%

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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“…Hence, there is a need to develop an extraction algorithm using fixed size ROI algorithm which is more practical compared to dynamic size palmprint ROI. Other than that, the number of features extracted using dynamic [12] are limited because the location of the extraction only focused at the center part of the palmprint. Therefore, there is a need to develop larger fixed size palmprint ROI extraction algorithm so that more features can be captured inside the extracted palmprint ROI.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fixed Size Palmprint Region of Interest (ROI) Extraction Algorithm for Personal Identification

Harun*,

Abdul Rahman,

Aliman

et al. 2019

IJRTE

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Identification and verification are the fundamental process in biometrics recognition system. Research indicates that palmprint, as one of the biometric recognitions system is commonly used for human identification. It is because there are many features and information contained inside the palmprint that can be used in the identification process. However, only a small region of the palmprint can be extracted using the existing palmprint region of interest (ROI) extraction algorithms. This has become a problem for identification systems due to negligible and loss of important features which are located outside the ROI. Hence, it is a necessity to improve the palmprint ROI extraction algorithm whereby bigger palmprint ROI can be extracted using this algorithm. Therefore, a larger fixed size extraction algorithm for palmprint ROI is proposed where the extraction region is larger so that more important identification features can be captured inside these ROIs. The performance between proposed and existing extraction algorithms are tested based on two characteristics which are the palmprint ROI extraction area and the comparison of feature creases extracted in a palmprint ROI. The results show that 300x300 fixed size ROI is able to capture 13 out of 14 creases attributes for palmprint identification. This implies that the proposed extraction algorithm shows a promising method of extraction as compared to the existing algorithms.

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