Deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices

Marasco, Emanuela; Vurity, Anudeep; Otham, Asem

doi:10.1007/978-3-031-11346-8_31

Cited by 8 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach finds the best deep representation for each color model and combines the corresponding individual outputs. The overall system's performance combining the top three color spaces achieved an EER of 2.12% [17]. Although multiple color spaces were used on the whole image, there were no experimental validations to prove independence among the color spaces.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

Fingerphoto morphing attack generation using texture descriptors based landmarks

Li,

Ramachandra

2024

Sci Rep

View full text Add to dashboard Cite

Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.

show abstract

Fingerphoto Presentation Attack Detection: Generalization in Smartphones

Marasco

Vurity

2021

2021 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices

Cited by 8 publications

References 16 publications

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

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

Fingerphoto morphing attack generation using texture descriptors based landmarks

Fingerphoto Presentation Attack Detection: Generalization in Smartphones

Contact Info

Product

Resources

About