A direct fingerprint minutiae extraction approach based on convolutional neural networks

We propose a novel architecture for generic biometric authentication based on deep neural networks: RegNet. Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a wellbehaved space in which users can be separated by means of simple and tunable boundaries. More specifically, authorized and unauthorized users are mapped onto two different and well behaved Gaussian distributions. The novel approach of learning the mapping instead of the boundaries further avoids the problem encountered in typical classifiers for which the learnt boundaries may be complex and difficult to analyze. RegNet achieves high performance in terms of security metrics such as Equal Error Rate (EER), False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). The experiments we conducted on publicly available datasets of face and fingerprint confirm the effectiveness of the proposed system.

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“…As an example, in [10] a Convolutional Neural Network (CNN) is used to extract minutiae from raw fingerprint images.…”

Section: A Related Workmentioning

confidence: 99%

Learning mappings onto regularized latent spaces for biometric authentication

Testa

Ali

Bianchi

et al. 2019

2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)

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“…For this reason, researchers use CNNs in different application scenarios, such as object classification [24] or natural language processing [22]. In biometrics, there are recognition methods based on CNNs for different traits, such as: face [6], iris [28,29], and fingerprint [20]. There are also liveness detection methods bases on CNNs and designed for a wide set of biometric characteristics [37,39,46].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Labati

Genovese

Muñoz

et al. 2018

Pattern Recognition Letters

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Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods.

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“…This new disk structure lucidly demonstrates the allocation of surrounding minutiae and trains the surrounding minutiae very competently. Jiang et al [11] introduced a convolutional neural-network-based approach for automatic extraction of minutiae points from fingerprint image. This approach does not utilize any preprocessing algorithm such as binarization, thinning and enhancement.…”

Section: Related Workmentioning

confidence: 99%

Rotation-invariant fingerprint matching using radon and DCT

Bharkad

Kokare

2017

Sādhanā

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A new set of promising rotation-invariant features based on radon and discrete cosine transform (DCT) is proposed for fingerprint matching. The radon and DCT of a tiny area in the region of core point of fingerprint image is computed. In the proposed method only 34% DCT coefficients are used for feature extraction. Competency of this approach is tested on standard databases, namely FVC2002 and FVC2004. This approach provides 70% genuine acceptance rate (GAR) at *0% false acceptance rate (FAR) and 95% GAR at 10% FAR on rotated and non-rotated databases, respectively. Experimental results prove that the proposed feature extraction approach is rotation invariant.

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A direct fingerprint minutiae extraction approach based on convolutional neural networks

Cited by 44 publications

References 26 publications

Learning mappings onto regularized latent spaces for biometric authentication

Learning mappings onto regularized latent spaces for biometric authentication

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Rotation-invariant fingerprint matching using radon and DCT

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