Segmentation of fingerprint images — A composite method

Mehtre, Babu M.; Chatterjee, B.

doi:10.1016/0031-3203(89)90047-2

Cited by 125 publications

(57 citation statements)

References 5 publications

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“…This segmentation method, which is called the directional method, has some limitations in the case of images with a perfectly uniform distribution of gray regions, as pointed out in [20]. A composite method using variance and directional criterion overcomes the difficulties associated with the region of uniform gray values.…”

Section: Ridge Segmentationmentioning

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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Section: Ridge Segmentationmentioning

confidence: 99%

A Robust Fingerprint Matching System Using Orientation Features

2015

J Inf Process Syst

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“…Concerning that offline palmprint images are much larger and more sensitive to the complexity of an algorithm than fingerprint images we propose a quick orientation field estimate algorithm based on Methre's algorithm [4] and, an orientation modification algorithm based on the information of Special Area.…”

Section: Overview Of Offline Palmprint Enhancement Algorithmmentioning

confidence: 99%

“…To improve the efficiency, Methre's method [4] is adopted to estimate orientation field. In addition, observing that orientations are continuous in small local regions, four pixels are considered as a whole when estimating orientation.…”

Section: Orientation Field Estimationmentioning

confidence: 99%

Research on Offline Palmprint Image Enhancement

Zheng

Shi

Zhang³

et al. 2007

2007 IEEE International Conference on Image Processing

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Palmprint identification, a subcategory of biometrics identification, has become a hot research area, and image enhancement is a key problem in offline palmprint identification. Since the physiological characteristics and image quality of palmprints are different from those of fingerprints, existing algorithms on fingerprint image enhancement cannot be directly applied in offline palmprint images. Taking into account the characteristics of palmprint images, an enhancement algorithm specific to offline palmprint images is proposed in this paper. We have performed a series of experiments and provide the enhanced palmprint images in the experiment section. Moreover, we evaluate our algorithm by comparing it with the method only using a low-pass filter to smooth the images under the criteria of GI value. Besides, the running time of each step is given to show the efficiency of the algorithm. The result shows that our algorithm is capable of attaining the objectives of offline palmprint enhancement efficiently.

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“…Block mean, block standard deviation, block gradient histogram [1,2], block average magnitude of the gradient [11] are most common block features for fingerprint segmentation. In [12] gray-level pixel intensity-derived feature called block clusters degree(CluD) is introduced.…”

Section: Features For Fingerprint Segmentationmentioning

confidence: 99%

“…There are two types of fingerprint segmentation algorithms: unsupervised and supervised. Unsupervised algorithms extract blockwise features such as local histogram of ridge orientation [1,2], gray-level variance, magnitude of the gradient in each image block [3], Gabor feature [4,5]. Practically, the presence of noise, low contrast area, and inconsistent contact of a fingertip with the sensor may result in loss of minutiae or more spurious minutiae.…”

Section: Introductionmentioning

confidence: 99%

Robust Point-Based Feature Fingerprint Segmentation Algorithm

Tulyakov

Govindaraju

Advances in Biometrics

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Abstract. A critical step in automatic fingerprint recognition is the accurate segmentation of fingerprint images. The objective of fingerprint segmentation is to decide which part of the images belongs to the foreground containing features for recognition and identification, and which part to the background with the noisy area around the boundary of the image. Unsupervised algorithms extract blockwise features. Supervised method usually first extracts point features like coherence, average gray level, variance and Gabor response, then a Fisher linear classifier is chosen for classification. This method provides accurate results, but its computational complexity is higher than most of unsupervised methods. This paper proposes using Harris corner point features to discriminate foreground and background. Shifting a window in any direction around the corner should give a large change in intensity. We observed that the strength of Harris point in the foreground area is much higher than that of Harris point in background area. The underlying mechanism for this segmentation method is that boundary ridge endings are inherently stronger Harris corner points. Some Harris points in noisy blobs might have higher strength, but it can be filtered as outliers using corresponding Gabor response. The experimental results proved the efficiency and accuracy of new method are markedly higher than those of previously described methods.

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Segmentation of fingerprint images — A composite method

Cited by 125 publications

References 5 publications

A Robust Fingerprint Matching System Using Orientation Features

A Robust Fingerprint Matching System Using Orientation Features

Research on Offline Palmprint Image Enhancement

Robust Point-Based Feature Fingerprint Segmentation Algorithm

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