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Spoofing Detection on Hand Images Using Quality Assessment

Asish Bera, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri and Hubert P. H. Shum
Multimedia Tools and Applications (MTAP), 2021

 Impact Factor: 3.6

Spoofing Detection on Hand Images Using Quality Assessment

Abstract

Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification error using the k-nearest neighbors and random forest classifiers. An average classification error of 2.5% is obtained using the baseline evaluation with the MobileNetV2 deep network for discriminating original and different types of fake hand samples.

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BibTeX

@article{bera21spoofing,
 author={Bera, Asish and Dey, Ratnadeep and Bhattacharjee, Debotosh and Nasipuri, Mita and Shum, Hubert P. H.},
 journal={Multimedia Tools and Applications},
 title={Spoofing Detection on Hand Images Using Quality Assessment},
 year={2021},
 volume={80},
 pages={28603--28626},
 numpages={24},
 doi={10.1007/s11042-021-10976-z},
 issn={1573-7721},
 publisher={Springer},
}

RIS

TY  - JOUR
AU  - Bera, Asish
AU  - Dey, Ratnadeep
AU  - Bhattacharjee, Debotosh
AU  - Nasipuri, Mita
AU  - Shum, Hubert P. H.
T2  - Multimedia Tools and Applications
TI  - Spoofing Detection on Hand Images Using Quality Assessment
PY  - 2021
VL  - 80
SP  - 28603
EP  - 28626
DO  - 10.1007/s11042-021-10976-z
SN  - 1573-7721
PB  - Springer
ER  - 

Plain Text

Asish Bera, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri and Hubert P. H. Shum, "Spoofing Detection on Hand Images Using Quality Assessment," Multimedia Tools and Applications, vol. 80, pp. 28603-28626, Springer, 2021.

Supporting Grants

Similar Research

Asish Bera, Debotosh Bhattacharjee and Hubert P. H. Shum, "Two-Stage Human Verification using HandCAPTCHA and Anti-Spoofed Finger Biometrics with Feature Selection", Expert Systems with Applications (ESWA), 2021
Arindam Kar, Sourav Pramanik, Arghya Chakraborty, Debotosh Bhattacharjee, Edmond S. L. Ho and Hubert P. H. Shum, "LMZMPM: Local Modified Zernike Moment Per-Unit Mass for Robust Human Face Recognition", IEEE Transactions on Information Forensics and Security (TIFS), 2021

 

 

Last updated on 17 February 2024
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