# Spoofing Detection on Hand Images Using Quality Assessment

arXiv: https://arxiv.org/abs/2110.12923 · HTML: https://mlsanity.com/paper/2110.12923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [Biometric Presentation Attack Detection: Beyond the Visible Spectrum](https://mlsanity.com/paper/1902.11065/) (2019)
- [Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems](https://mlsanity.com/paper/1609.01461/) (2016)
- [Thermal Features for Presentation Attack Detection in Hand Biometrics](https://mlsanity.com/paper/1809.04364/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Neural Networks Fused with Textures for Image Classification](https://mlsanity.com/paper/2308.01813/) (2023)

## Abstract (arXiv)

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 er ror 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.