Fake Finger Detection by Finger Color Change Analysis

Yau, Wei-Yun; Tran, Hoang-Thanh; Teoh, Eam Khwang; Wang, Jiangang

doi:10.1007/978-3-540-74549-5_93

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…A second group of fingerprint liveness detection techniques has appeared as an application of the different fingerprint distortion models described in the literature [16,9,21]. These models have led to the development of a number of liveness detection techniques based on the flexibility properties of the skin [22,6,113,111]. In most of these works the user is required to move his finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion.…”

Section: Early Work In Fingerprint Presentation Attack Detectionmentioning

confidence: 99%

Introduction to Presentation Attack Detection in Fingerprint Biometrics

Galbally

Fiérrez

Cappelli

et al. 2023

Advances in Computer Vision and Pattern Recognition

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Section: Early Work In Fingerprint Presentation Attack Detectionmentioning

confidence: 99%

Introduction to Presentation Attack Detection in Fingerprint Biometrics

Galbally

Fiérrez

Cappelli

et al. 2023

Advances in Computer Vision and Pattern Recognition

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“…In the case of dynamic approaches, published studies utilize temporal analysis to capture the physiological features, such as perspiration [22,16], blood flow [31,14], skin distortion [1], and color change [31,23]. Table 1 summarizes the dynamic approaches for fingerprint spoof detection reported in the literature.…”

Section: Studymentioning

confidence: 99%

Fingerprint Spoof Detection: Temporal Analysis of Image Sequence

Chugh¹,

Jain²

2019

Preprint

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We utilize the dynamics involved in the imaging of a fingerprint on a touch-based fingerprint reader, such as perspiration, changes in skin color (blanching), and skin distortion, to differentiate real fingers from spoof (fake) fingers. Specifically, we utilize a deep learning-based architecture (CNN-LSTM) trained end-to-end using sequences of minutiae-centered local patches extracted from ten color frames captured on a COTS fingerprint reader. A timedistributed CNN (MobileNet-v1) extracts spatial features from each local patch, while a bi-directional LSTM layer learns the temporal relationship between the patches in the sequence. Experimental results on a database of 26, 650 live frames from 685 subjects (1, 333 unique fingers), and 32, 910 spoof frames of 7 spoof materials (with 14 variants) shows the superiority of the proposed approach in both known-material and cross-material (generalization) scenarios. For instance, the proposed approach improves the state-of-the-art cross-material performance from TDR of 81.65% to 86.20% @ FDR = 0.2%.

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“…They found that the application of force produced blood perfusion coloration patterns beneath the fingernail that were common among all people in general [10]. Previous work by Wei-Yun Yau, Hoang-Thanh Tran, Eam-Khwang Teoh, and Jian-Gang Wang demonstrated the viability of detecting fake fingers using finger color change by developing and testing a method of measuring blood perfusion when a finger is applied to a sensor [16]. The data set used for testing was very small, was not generated using a commercial fingerprint sensor, and contained only one type of spoof finger.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures

Plesh

Bahmani

Jang

et al. 2019

2019 International Conference on Biometrics (ICB)

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Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with timeseries and color-sensing capabilities to improve the robustness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable.

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Fake Finger Detection by Finger Color Change Analysis

Cited by 13 publications

References 9 publications

Introduction to Presentation Attack Detection in Fingerprint Biometrics

Introduction to Presentation Attack Detection in Fingerprint Biometrics

Fingerprint Spoof Detection: Temporal Analysis of Image Sequence

Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures

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