A Scheme for Fingerphoto Recognition in Smartphones

Labati, Ruggero Donida; Genovese, Angelo; Piuri, Vincenzo; Scotti, Fabio

doi:10.1007/978-3-030-26972-2_3

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…However, existing reviews lack a comprehensive discussion of current approaches. Notably, Malhotra et al [8], Mil'shtein and Pillai [9], and Labati et al [10] have explored various facets of touchless fingerprint recognition, providing valuable insights into mobile touchless recognition, comparative reviews, and a comprehensive overview of the entire recognition pipeline.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Sensor Based Touchless Fingerprint System

Khan,

Al-Kaabi,

Al-Kharusi

et al. 2024

jour

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his paper introduces a touchless fingerprint recognition system, offering a hygienic alternative to conventional touch-based systems. The primary objective is to develop and evaluate a comprehensive solution that addresses hygiene-related concerns and mitigates the risks associated with disease transmission, particularly in the context of the ongoing Covid-19 pandemic. The proposed system utilizes a state-of-the-art touchless sensor to capture intricate fingerprint images, subsequently processing, storing, and retrieving them for matching purposes. By eliminating the need for physical contact, the system effectively tackles issues related to inconsistent pressure and prolonged collection times. The touchless fingerprint technology showcased in this system holds great promise for diverse applications that demand secure and hygienic fingerprint recognition. The proposed method overcomes the limitations inherent in touch-based systems, making it well-suited for environments where health and safety considerations are paramount. The integration of this touchless technology not only enhances security but also contributes significantly to public health by reducing the risk of cross-contamination in shared touchpoints. As the world navigates the challenges posed by the pandemic, the adoption of touchless fingerprint recognition systems represents a crucial step toward creating safer and more hygienic environments.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Sensor Based Touchless Fingerprint System

Khan,

Al-Kaabi,

Al-Kharusi

et al. 2024

jour

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“…Under constrained capture conditions NFIQ2 is found to be an effective tool for touchless fingerprint quality estimation if an adequate preprocessing is applied. We will come back later to the topic of identification of individual from friction ridge skin images gathered from social media, but it is worth mentioning here that biometric systems are developed to handle verification using selfies taken from fingers [ 120 , 121 ].…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…In 2019, Labati et al discussed the finger photo recognition pipeline, including acquisition from a smartphone, liveness detection, quality assessment, segmentation, enhancement, feature extraction, and matching. The authors concluded that the two main reasons for the low performance of matching are due to heterogeneous smartphone acquisition, uncontrolled illumination, and background conditions' diversity [16].…”

Section: Related Workmentioning

confidence: 99%

Late Deep Fusion of Color Spaces to Enhance Finger Photo Presentation Attack Detection in Smartphones

Marasco¹,

Vurity²

2022

Applied Sciences

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Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art.

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A Scheme for Fingerphoto Recognition in Smartphones

Cited by 9 publications

References 35 publications

Smart Sensor Based Touchless Fingerprint System

Smart Sensor Based Touchless Fingerprint System

Interpol review of fingermarks and other body impressions (2019 – 2022)

Late Deep Fusion of Color Spaces to Enhance Finger Photo Presentation Attack Detection in Smartphones

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