Spoofing Detection on Hand Images Using Quality Assessment
Asish Bera, Ratnadeep Dey, Debotosh Bhattacharjee, Mita Nasipuri, and, Hubert P. H. Shum

TL;DR
This paper introduces a novel quality assessment method based on gradient magnitude similarity to detect spoofed hand images, achieving high accuracy in distinguishing genuine from fake samples using various classifiers.
Contribution
It proposes a new gradient similarity-based quality metric for anti-spoofing in hand biometrics, effective against natural and artificially degraded fake hand images.
Findings
Gradient similarity metric achieves 1.5% classification error with KNN and Random Forest.
Deep CNN (MobileNetV2) achieves 2.5% error in fake hand detection.
Method effectively discriminates genuine and spoofed hand images with high accuracy.
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…
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Taxonomy
MethodsDepthwise Convolution · Pointwise Convolution · Depthwise Separable Convolution · Batch Normalization · Convolution · Average Pooling · Inverted Residual Block · 1x1 Convolution
