Finger-vein presentation attack detection using depthwise separable convolution neural network

Biometrics is a powerful tool for identifying and authenticating persons based on their unique characteristics. Finger vein (FV) seems to be an emerging biometric of all types of hand-based biometrics, which have garnered considerable interest because of the extensive information and ease of implementation. As the FV system has grown in popularity, there have been numerous attempts to compromise it. Recent research reveals that the finger-vein recognition (FVR) system is vulnerable to presentation attacks, in which the sensory device accepts a fake printed FV image and grants access as if it were a genuine attempt. Few deep learning (DL) studies were developed in the past to detect spoof attacks in FV images. Existing works rely on performance improvement by neglecting the problems of the limited dataset, high computational complexity, and unavailability of lightweight and efficient feature descriptors. Therefore, we have proposed a novel depthwise separable convolution neural network (DSC) with residual connection and a linear support vector machine (LSVM) for the automatic detection of FV presentation attacks. At first, we apply the data augmentation method to raw images to increase the size of datasets. Afterward, this DSC technique is applied to extract robust features from FV images. Finally, a LSVM classifier is used to classify the images into bonafide and fake images. The proposed DSC-LSVM method is evaluated on two publicly available datasets such as Idiap and SCUT-FVD. The experimental results show that the DSC-SVM model attained a low error rate of 0.00% for presentation attack detection (PAD) on both datasets compared to state-of-the-art approaches. Several experimental results show that the DSC-LSVM model required less computation time and fewer parameters to detect spoof finger vein images. It concludes that the DSC-LSVM system is outperformed for PAD on finger vein images compared to existing methods.