Preprocessing of Fingerprint Images Captured with a Digital Camera

Hiew,; Teoh, Andrew Beng Jin; Ngo, David Chek Ling

doi:10.1109/icarcv.2006.345189

Cited by 26 publications

(17 citation statements)

References 10 publications

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“…[15,16] have shown that is possible to extract a fingerprint by using a low-cost webcam when applying different preprocessing and image enhancements approaches. [17] suggested a preprocessing approach for fingerprint recognition.…”

Section: Related Workmentioning

confidence: 99%

Securing Mobile Cloud Using Finger Print Authentication

Rassan¹,

AlShaher²

2013

IJNSA

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Section: Related Workmentioning

confidence: 99%

Securing Mobile Cloud Using Finger Print Authentication

Rassan¹,

AlShaher²

2013

IJNSA

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“…For this reason, most of the contactless recognition systems in the literature use illumination techniques to improve the visibility of the ridge pattern, like a point light source [8][9][10] or ring illuminators [19]. In the literature, there are also studies on the light wavelengths that permit to enhance the visibility of the ridge pattern [11,20].…”

Section: Related Workmentioning

confidence: 99%

“…Then, a background subtraction algorithm and a cutoff filter tuned according to the mean ridge frequency are applied. Differently, the image enhancement method proposed in [8][9][10] adopts a contextual filtering technique based on the STFT analysis [24]. Similarly to the technique presented in [25], the method described in [7] applies Gabor filters tuned according to the local ridge frequency and orientation, but it computes the ridge orientation map by using an iterative regression algorithm designed to be more robust to the noise present in contactless fingerprint images.…”

Section: Related Workmentioning

confidence: 99%

“…These systems usually adopt matching algorithms designed for contact-based images [4,5,[7][8][9][10][11]23]. These algorithms however, require fingerprint images with known resolution and acquired with controlled rotations.…”

Section: Related Workmentioning

confidence: 99%

“…Contactless fingerprint recognition systems based on single CCD cameras therefore result more suitable. Examples of this kind of biometric systems are described in [4,5,[7][8][9][10]. Most of the existing technologies, however, are not able to obtain accuracy levels comparable to contact-based systems since they do not use feature extraction and matching techniques specifically designed to overcome the typical non-idealities of contactless samples.…”

Section: Introductionmentioning

confidence: 99%

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Contactless fingerprint recognition: A neural approach for perspective and rotation effects reduction

Labati

Genovese

Piuri

et al. 2013

2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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Abstract-Contactless fingerprint recognition systems are being researched in order to reduce intrinsic limitations of traditional biometric acquisition technologies, encompassing the release of latent fingerprints on the sensor platen, non-linear spatial distortions in the captured samples, and relevant image differences with respect to the moisture level and pressure of the fingertip on the sensor surface.Fingerprint images captured by single cameras, however, can be affected by perspective distortions and deformations due to incorrect alignments of the finger with respect to the camera optical axis. These non-idealities can modify the ridge pattern and reduce the visibility of the fingerprint details, thus decreasing the recognition accuracy. Some systems in the literature overcome this problem by computing three-dimensional models of the finger. Unfortunately, such approaches are usually based on complex and expensive acquisition setups, which limit their portability in consumer devices like mobile phones and tablets.In this paper, we present a novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems. The approach estimates the orientation difference between two contactless fingerprint acquisitions by using neural networks, and permits to register the considered samples by applying the estimated rotation angle to a synthetic three-dimensional model of the finger surface. The generalization capability of neural networks offers a significant advantage by allowing processing a robust estimation of the orientation difference with a very limited need of computational resources with respect to traditional techniques. Experimental results show that the approach is feasible and can effectively enhance the recognition accuracy of single-camera biometric systems. On the evaluated dataset of 800 contactless images, the proposed method permitted to decrease the equal error rate of the used biometric system from 3.04% to 2.20%.

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