Latent Fingerprint Enhancement Based On Directional Total Variation Model With Lost Minutiae Reconstruction

Liban, Abdilahi; Hilles, Shadi M. S.

doi:10.1109/icscee.2018.8538417

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…The result indicates 0.799 second processing time has been reduced. Furthermore, Liban and Hilles [31] suggest enhancing latent fingerprint to improve fingerprint quality so that reasonable processing time would be achieved. However, Koptyra and Ogiela [32] argued that higher fingerprint processing time will be achieved if enhancing Histograms of Oriented Gradients (HOG) technique.…”

Section: 1results Of Fingerprint Biometric Authenticationmentioning

confidence: 99%

A survey of biometric approaches of authentication

Yusuf¹,

Marafa²,

Shehu³

et al. 2020

IJACR

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Information security refers to a means of preventing unauthorized users from access to information. Risk management usually adopts in information security to provide solutions to security challenges by minimizing risks.Risk can be minimized by administrative control and defense mechanisms. Access control can also enforce the user right access and thereby minimizing risks. Authentication is one of the techniques used in access control systems to protect unauthorized access. Many approaches for authentication have been proposed to address security challenges. These approaches have a conflict with the system usability and therefore required the modification of tradition password techniques for better solutions. Information Authentication is the common method used by security experts to verify the users' identities before getting access right into the system. Access controls are enforced for all users, irrespective of categories they belong. Traditional authentication methods [1] were enough to protect the unauthorized access right as many security breaches were reported. Therefore, advanced security methods that are based on human features required.

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Section: 1results Of Fingerprint Biometric Authenticationmentioning

confidence: 99%

A survey of biometric approaches of authentication

Yusuf¹,

Marafa²,

Shehu³

et al. 2020

IJACR

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“…The working of the algorithm comprises of five steps namely fingerprint acquisition, fingerprint pre-processing, fingerprint enhancement, feature extraction and minutiae matching (Liban and Hilles, 2018).…”

Section: Minutiae Extraction and Comparison Algorithmmentioning

confidence: 99%

AI Based Mobile Bill Payment System using Biometric Fingerprint

Kathirvel¹,

Sudha²,

Naveneethan³

et al. 2022

American Journal of Engineering and Applied Sciences

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Billions of payment transactions occur in our day-to-day life, but every payment method depends on a material to carry. It is common for users to possess various materials like cash, credit cards and even mobiles to make payment. Meanwhile, it is easy for these materials to be robbed or lost. These instances result in a terrible trauma for the people. This study gives a detailed portrayal of a biometric payment application developed to introduce a concept of material-less payment. It enables the user to make a payment at any location by enrolling their fingertip without possessing any material. It involves a one-time registration of the User details upon all further transactions are validated and processed based on the user's fingerprint where the App takes care of the whole process. This implementation results in a novel payment method and avoids the risk of carrying valuable materials outdoors. This App creates an efficient and safe payment for society.

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“…In converge phase, the search agent converge towards the food source (best search agent). (20) However, in tunicate swarm behaviour phase, the first dual optimal best solutions gets saved and other search agents positions are updated based on the best search agent position.…”

Section: Intrinsic Feature Extraction-optimized Bi-cnnmentioning

confidence: 99%

“…Typically, latent fingerprints are poor quality with the terms of ridge structure and high background noise which leads to unreliable feature extraction. Hence, identification of these fingerprints is more challenging one for forensic agencies in order to recognize the suspected persons who committed a crime [Chaidee et al (2018)] and [Liban and Hilles (2018)].…”

Section: Introductionmentioning

confidence: 99%

Latent Fingerprint Image Enhancement Based on Optimized Bent Identity Based Convolutional Neural Network

Chaudhary¹,

Dimri²

2021

INDJCSE

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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.

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Latent Fingerprint Enhancement Based On Directional Total Variation Model With Lost Minutiae Reconstruction

Cited by 12 publications

References 8 publications

A survey of biometric approaches of authentication

A survey of biometric approaches of authentication

AI Based Mobile Bill Payment System using Biometric Fingerprint

Latent Fingerprint Image Enhancement Based on Optimized Bent Identity Based Convolutional Neural Network

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