Wavelet domain features for fingerprint recognition

Tico, Marius; Kuosmanen, Pauli; Saarinen, Jukka

doi:10.1049/el:20010031

Cited by 89 publications

(47 citation statements)

References 4 publications

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“…Tico et al [13,18] proposed a method for fingerprint recognition based on local texture features extracted from the wavelet transform of a discrete image. The ROI of 64×64 pixels is cropped around the core point (detected manually).…”

Section: Related Workmentioning

confidence: 99%

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A Robust Fingerprint Matching System Using Orientation Features

2015

J Inf Process Syst

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The latest research on the image-based fingerprint matching approaches indicates that they are less complex than the minutiae-based approaches when it comes to dealing with low quality images. Most of the approaches in the literature are not robust to fingerprint rotation and translation. In this paper, we develop a robust fingerprint matching system by extracting the circular region of interest (ROI) of a radius of 50 pixels centered at the core point. Maximizing their orientation correlation aligns two fingerprints that are to be matched. The modified Euclidean distance computed between the extracted orientation features of the sample and query images is used for matching. Extensive experiments were conducted over four benchmark fingerprint datasets of FVC2002 and two other proprietary databases of RFVC 2002 and the AITDB. The experimental results show the superiority of our proposed method over the well-known image-based approaches in the literature.

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Section: Related Workmentioning

confidence: 99%

“…This method involves steps that are almost similar to the ones used by Tico's method, except that the feature vector length is six per block (or a total length of 24 for four sub-images) instead of 48. However, the two features in [5,13,18] are not robust to rotation.…”

Section: Related Workmentioning

confidence: 99%

A Robust Fingerprint Matching System Using Orientation Features

2015

J Inf Process Syst

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“…Jain et al proposed a well-known image-based method: FingerCode [15], [16], which extracted the image feature by applying Gabor filters in the ROI around the core point. Other filter bank based methods were proposed based on Discrete Cosine Transform (DCT) in [17] and Discrete Wavelet Transform (DWT) in [18]- [20]. Nanni and Lumini proposed a hybrid fingerprint descriptor based on Local BiCopyright c 2017 The Institute of Electronics, Information and Communication Engineers nary Pattern (LBP) from the image convolved with Gabor filters [21].…”

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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“…All of the above methods have more or less several imperfections. Some of these types of imperfections include: the performance of an algorithm and its computational speed degrades when the image quality is not satisfactory, for example the case of a multi-spectral noisy image [5]; a limited efficiency when a different scale position and rotation angle are used for the same input images [15]; or suffering from time-consuming alignment (correlation-based).…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

Basak¹,

Islam²,

Amin³

2012

Journal of Information Processing Systems

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Abstract-Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

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Wavelet domain features for fingerprint recognition

Cited by 89 publications

References 4 publications

A Robust Fingerprint Matching System Using Orientation Features

A Robust Fingerprint Matching System Using Orientation Features

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

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