An investigation of fake fingerprint detection approaches

Ahmad, Abdul-Rahim; Hassan, Rohayanti; Othman, Razib M.

doi:10.1063/1.5005353

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…An investigation of fake fingerprint detection approaches [11], the author perform a detailed review on various methods of fake finger print detection. Number of research articles has been considered and based on that various taxonomy of fake prints has been generated.…”

Section: Related Workmentioning

confidence: 99%

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar

Devi

Ramkumar

et al. 2021

Neural Process Lett

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The problem of forgery detection has been well studied and the forged finger prints produces highly impacting results in the biometric based security systems. There are many algorithms discussed earlier to detect forged finger prints. However, they suffer to achieve higher performance in terms of security. In this paper, a region centric minutiae propagation measure (RCMPM) based approach. First, the finger print image is read and removes the noisy points by applying the multi level Gabor filters. The Gabor filter has been applied in multiple level which helps to remove the noise from finger print image. The enhanced image is converted into number of integral image. The integral images are generated by splitting the image into number of tiny images according to the size of window. From the integral image produced, the island, dot, enclosure, bifurcation features are extracted. Extracted features are framed as feature vector and used to estimate the RCMPM measure. Based on the RCMPM measure, the presence of forged finger print has been identified and the same has been used to identify the region which has been modified. The accuracy of forged print detection has been improved and reduces the false classification ratio.

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Section: Related Workmentioning

confidence: 99%

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar

Devi

Ramkumar

et al. 2021

Neural Process Lett

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“…In [10], the author presents a detailed analysis of various fingerprint analysis schemes and fake print detection schemes. Also, a taxonomy of approaches is generated.…”

Section: Plos Onementioning

confidence: 99%

Multi-region minutiae depth value-based efficient forged finger print analysis

Baskar,

Rajagopal,

B. V. V. S.

et al. 2023

PLoS ONE

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The application of biometrics has expanded the wings to many domains of application. However, various biometric features are being used in different security systems; the fingerprints have their own merits as it is more distinct. A different algorithm has been discussed earlier to improve the security and analysis of fingerprints to find forged ones, but it has a deficiency in expected performance. A multi-region minutiae depth value (MRMDV) based finger analysis algorithm has been presented to solve this issue. The image that is considered as input has been can be converted into noisy free with the help of median and Gabor filters. Further, the quality of the image is improved by sharpening the image. Second, the preprocessed image has been divided into many tiny images representing various regions. From the regional images, the features of ridge ends, ridge bifurcation, ridge enclosure, ridge dot, and ridge island. The multi-region minutiae depth value (MRMDV) has been computed based on the features which are extracted. The test image which has a similarity to the test image is estimated around MRMDV value towards forgery detection. The MRMDV approach produced noticeable results on forged fingerprint detection accuracy up to 98% with the least time complexity of 12 seconds.

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“…The FPAD approaches are mainly based on local features that include Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF) [6] and global features that are based on deep learning methods [7]. In a recent work, researchers used a middle approach of FPAD using minutiae-centred patches with deep learning [8].…”

Section: Related Workmentioning

confidence: 99%

Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

Verma¹,

Gupta²,

Akbar³

et al. 2021

Preprint

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The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

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An investigation of fake fingerprint detection approaches

Cited by 6 publications

References 32 publications

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Multi-region minutiae depth value-based efficient forged finger print analysis

Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

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