Palmprint feature extraction using 2-D Gabor filters

Kong, Wai-Kin; Zhang, David; Li, Wenxin

doi:10.1016/s0031-3203(03)00121-3

Cited by 324 publications

(146 citation statements)

References 21 publications

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“…Total processing time of the developed algorithm is currently 50-67 milliseconds per frame. In order to minimize the tracking errors of AVP, preprocessing of fluoroscopic images can be further improved by using a 2D Gabor filter [25]. Also, we aim to track the aortic valve landmarks, e.g.…”

Section: Discussionmentioning

confidence: 99%

Aortic valve prosthesis tracking for transapical aortic valve implantation

et al. 2010

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Purpose Transapical aortic valve implantation (TA-AVI) is a new minimally invasive surgical treatment of aortic stenosis for high risk patients. The placement of aortic valve prosthesis (AVP) is performed under 2D Xray fluoroscopic guidance. Difficult clinical complications can arise if the implanted valve is misplaced. Therefore we present a method to track the AVP in 2D X-ray fluoroscopic images in order to improve the accuracy of the TA-AVI. MethodsThe proposed tracking method includes the template matching approach to estimate the position of AVP and a shape model of the prosthesis to extract the corner points of the AVP in each image of sequence. To start the AVP tracking procedure, an initialization step is performed by manually defining the corner points of the prosthesis in the first image of sequence to provide the required algorithm parameters such as the AVP model parameters.Results We evaluated the AVP tracking method on six 2D intra-operative fluoroscopic image sequences. The results of automatic AVP localization agree well with manually-defined AVP positions. The maximum localization errors of tracked prosthesis are less than 1 mm and within the clinical accepted range.Conclusions For assisting the TA-AVI, a method for tracking the AVP in 2D X-ray fluoroscopic image sequences has been developed. Our AVP tracking method is a first step towards automatic optimal placement of the AVP during the TA-AVI.

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Section: Discussionmentioning

confidence: 99%

Aortic valve prosthesis tracking for transapical aortic valve implantation

et al. 2010

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“…Since the Gabor filtered representation can provide the optimal localization of image details, we choose a group of Gabor functions to perform a joint spatial-frequency multi-channel transform on the palmprint image, which can be expressed as following [2]:…”

Section: Gabor Statistical Feature Extractionmentioning

confidence: 99%

“…For typical (unprotected) palmprint verification systems, there have been many feature representation approaches reported achieving high verification accuracy. Among them there are some coding based methods which generate binary features [2,3]. However, most of them require a template registration during the matching stage, which might be not allowed for template protection system.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, a fixed-length binary representation also has additional advantages such as small template storage and high matching speed. Since the over-complete information of Gabor filtering responses contributes to the discriminating ability, our method chooses to filter the palmprint image by a group of two-dimensional Gabor functions firstly [2]. Figure 1 shows the flow chart of our proposed binary feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

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Binary Gabor Statistical Features for Palmprint Template Protection

Ruan

Shao

et al. 2012

Lecture Notes in Computer Science

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Abstract. The biometric template protection system requires a highquality biometric channel and a well-designed error correction code (ECC). Due to the intra-class variations of biometric data, an efficient fixed-length binary feature extractor is required to provide a high-quality biometric channel so that the system is robust and accurate, and to allow a secret key to be combined for security. In this paper we present a binary palmprint feature extraction method to achieve a robust biometric channel for template protection system. The real-valued texture statistical features are firstly extracted based on Gabor magnitude and phase responses. Then a bits quantization and selection algorithm is introduced. Experimental results on the HongKong PloyU Palmprint database verify the efficiency of our method which achieves low verification error rate by a robust palmprint binary representation of low bit error rate.

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“…2. The Gabor feature with its derivatives have been successfully applied in fingerprint, palmprint [26], [27], face [28], [29], iris [30], [31], finger-vein [32] feature representation and recognition. In this work, we proposed a compact representation solution called Gabor Binary Code (GBC), which encoding the image patches after Gabor filtering by a series of pixel tests and yielding a fixed-length binary code.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

Isshiki

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYNarrow swipe sensor based systems have drawn more and more attention in recent years. However, the size of captured image is significantly smaller than that obtained from the traditional area fingerprint sensor. Under this condition the available minutiae number is also limited. Therefore, only employing minutiae with the standard associated feature can hardly achieve high verification accuracy. To solve this problem, we present a novel Hybrid Minutiae Descriptor (HMD) which consists of two modules. The first one: Minutiae Ridge-Valley Orientation Descriptor captures the orientation information around minutia and also the trace points located at associated ridge and valley. The second one: Gabor Binary Code extracts and codes the image patch around minutiae. The proposed HMD enhances the representation capability of minutiae feature, and can be matched very efficiently. Experiments conducted over public databases and the database captured by the narrow swipe sensor show that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate). key words: narrow swipe sensor, fingerprint verification, hybrid minutiae descriptor, Gabor feature

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Palmprint feature extraction using 2-D Gabor filters

Cited by 324 publications

References 21 publications

Aortic valve prosthesis tracking for transapical aortic valve implantation

Aortic valve prosthesis tracking for transapical aortic valve implantation

Binary Gabor Statistical Features for Palmprint Template Protection

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

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