  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.
