Fingerprint recognition using DCT features

Amornraksa, Thumrongrat; Tachaphetpiboon, S.

doi:10.1049/el:20064330

Cited by 60 publications

(29 citation statements)

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“…The similarity is calculated using the Euclidian distance between the feature vectors of the test and the trained fingerprint images. Amornraksa and Tachaphetpiboon [19] have also proposed a local texture analysis method based on the discrete cosine transform (DCT) for fingerprint matching. This method involves steps that are almost like 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.…”

Section: Related Workmentioning

confidence: 99%

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Correlated Feature Selection Model based on Swarm Intelligence to Enhance Rotational Invariant Fingerprint Authentication using Firefly Optimized Algorithm

Al-Azzawi¹

2016

International Journal of Advanced Research in Computer and Comm

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Fingerprint characteristic of every human are unique and stable, therefore widely accepted for personal identification. Accurate and reliable fingerprint recognition is a challenging task, which heavily depends on the quality of the fingerprint images. It is well-known that the fingerprint recognition systems are very sensitive to noise and other image transformations such as rotation. In this paper an enhanced algorithm is introduced to make the matching process rotational invariant. The proposed algorithm is robust to match two identical fingerprint images which are spatially aligned at different rotational angles and gives better FMR and FNMR ratio for images rotated at different angles. A Biometric Fingerprint authentication algorithm using firefly algorithm (FA) based feature selection approach which we named firefly optimization algorithm (FOA) is proposed to address the above issues.The search in (FOA) is iteratively guided by a fitness function defined to maximize class separation to identify new features instead of the traditional Minutiae (Termination and Bifurcation features). The first contribution is the formulation of a new feature selection algorithm for fingerprint recognition based on the DWT algorithm, which solves the localization problem by applying (FOA) separately to four sub-bands to increase the recognition rate and to speed up feature selection. The second contribution is an invariant moment matching algorithm which is proposed as a matching algorithm to address some misclassified features and to increase the matching accuracy. The proposed algorithm was found to generate excellent recognition results which admit 100% accuracy when applied on the FCV2002, 2004 and 2006 dataset. Also, it admits accuracy ranged 96.35-100% when applied to fingerprints with a rotation of 0-360º.

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Section: Related Workmentioning

confidence: 99%

“…LBP, local binary pattern based descriptor [18]. DCT, discrete cosine transform based fingerprint matcher [19]. WFMT, Fourier-Mellin transform based matcher [20].…”

Section: B Overall Dataset Performancementioning

confidence: 99%

Correlated Feature Selection Model based on Swarm Intelligence to Enhance Rotational Invariant Fingerprint Authentication using Firefly Optimized Algorithm

Al-Azzawi¹

2016

International Journal of Advanced Research in Computer and Comm

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“…Amornraksa and Tachaphetpiboon [5] have also proposed a local texture analysis method based on the discrete cosine transform (DCT) for fingerprint matching. 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.…”

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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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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“…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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Fingerprint recognition using DCT features

Cited by 60 publications

References 1 publication

Correlated Feature Selection Model based on Swarm Intelligence to Enhance Rotational Invariant Fingerprint Authentication using Firefly Optimized Algorithm

Correlated Feature Selection Model based on Swarm Intelligence to Enhance Rotational Invariant Fingerprint Authentication using Firefly Optimized Algorithm

A Robust Fingerprint Matching System Using Orientation Features

Hybrid Minutiae Descriptor for Narrow Fingerprint Verification

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