Palm region extraction for contactless palmprint recognition

Ito, Koichi; Sato, Toshiki; Aoyama, Satoshi; Sakai, Shuji; Yusa, S.; Aoki, Takafumi

doi:10.1109/icb.2015.7139058

Cited by 27 publications

(40 citation statements)

References 13 publications

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“…In addition to the above palmprint KP localization methods, there are also some other methods based on radial distance function [42,43], line fitting [44,45], and morphological corner detection [46]. However, these methods could have low localization accuracy in complex environment, or low applicable range.…”

Section: Non-contact Palmprint Segmentation and Localizationmentioning

confidence: 99%

Key Point Localization Based on Intersecting Circle for Palmprint Preprocessing in Public Security

Zhou¹,

Chen²,

Leng³

2019

J. Def. Acquis. Technol.

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Secure identity authentication is important in defense technology fields and public security. Palmprint is a good biometric modality used for authentication on mobile devices. Unfortunately, the background of palmprint image collected with mobile devices is random and changeable. Usually, the region of interest is localized based on the key points of the inter-finger-valley. In order to resist the random and variable background interference and effectively remove the false inter-finger-valley contours, this paper develops a key point localization method based on intersecting circle for palmprint preprocessing. Since the contour curvature of inter-finger-valley is greater than that of the rest of the hand, each contour point is taken as the center of an intersecting circle, and the radius of the intersecting circle is set according to the finger width. The developed method can determine the set of candidate contour points at the inter-finger-valley. The remained false inter-finger-valley contour points are further removed according to the direction of the central angle. The bottom point of the contour at the inter-finger-valley is used as the key point. The sufficient experiments show that the method in this paper is less dependent on the segmentation effect, and it is robust to false contour and background interference, so the localization and recognition accuracies are high.

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Section: Non-contact Palmprint Segmentation and Localizationmentioning

confidence: 99%

Key Point Localization Based on Intersecting Circle for Palmprint Preprocessing in Public Security

Zhou¹,

Chen²,

Leng³

2019

J. Def. Acquis. Technol.

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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%

“…Although these methods are famous and widely used, they depend on the gaps between the fingers as reference points to determine the coordinate system, which means that all fingers must be spread and the hand should be facing toward the camera. To tackle this problem, Ito et al [24] proposed a five-step method that can be summarized as follows: (i) binarization of the input image, (ii) combination of binarized image and edge, (iii) key point candidate detection using the radial-distance function, (iv) optimal key-point selection, and (v) palm region extraction. This approach suffers from the probability of important features loss due to small extracted ROIs.…”

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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“…Experimental evaluation using palmprint image databases demonstrates the efficient performance of the proposed algorithm compared with conventional algorithms. We have addressed one of challenging issues in palmprint recognition 72) . Accurate ROI extraction is indispensable in contactless authentication, since the performance of contactless palmprint recognition significantly depends on the accuracy of ROI extraction.…”

Section: Applicationsmentioning

confidence: 99%

“…The public palmprint databases such as PolyU palmprint database and CASIA palmprint database are also constructed based on the above assumption. Addressing the above problem and realizing practical contactless palmprint recognition, we proposed an accurate and robust palm region extraction method 72) . The proposed method employs the combination of image binarization and edge detection to detect keypoints as shown in Fig.…”

Section: Applicationsmentioning

confidence: 99%

[Invited Paper] Recent Advances in Biometric Recognition

Ito

Aoki

2018

MTA

Self Cite

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This paper presents recent advances in biometric recognition, where we focus on face, fingerprint and iris recognition, which are major research topics on biometric recognition. We summarize the research trend of face, fingerprint and iris recognition over the past decade. This paper also presents our activities of biometric recognition. Our approach employs the phase information obtained by Discrete Fourier Transform (DFT) of images. The phase information preserves the inherent features of the image, and its correlation function, called phase correlation or Phase-Only Correlation (POC), gives us both the good similarity measure for biometric recognition and the translational displacement for image registration.Our approach of using phase information has been successfully applied to fingerprint, face, iris, palmprint, finger knuckle and dental recognition. Among them, we present some interesting results of palmprint recognition, finger knuckle recognition and dental recognition.

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Palm region extraction for contactless palmprint recognition

Cited by 27 publications

References 13 publications

Key Point Localization Based on Intersecting Circle for Palmprint Preprocessing in Public Security

Key Point Localization Based on Intersecting Circle for Palmprint Preprocessing in Public Security

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

[Invited Paper] Recent Advances in Biometric Recognition

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