Finger vein presentation attack detection (FVPAD) biometric systems have seen substantial enhancements through the application of deep learning convolutional neural networks (DCNN). This advancement led to increased complexity, parameters and resource requirements. To address these challenges, a novel modification to the first entry flow of the XceptionNet architecture based on customized depthwise separable convolution (DSC) CNN-based for extracting robust features from FV images to detect spoofing attacks is proposed in this paper. The proposed approach stands out for its simplicity in design, fewer parameters, reduced computational load, minimal resource and equipment needs, and minimum data overflow while maintaining high accuracy in verification and classification tasks. The developed FVPAD system includes FV image data preprocessing and augmentation, a modified XceptionNet architecture based on DSC to deeply extract robust features. Finally, the fully connected (FC) layers exclusively use the SoftMax activation function to normalize, predict and classify output classes. The model was evaluated on cropped FV images from the IDIAP and SCUT-SFVD datasets, achieving high accuracy rates of 100% and 99.499%, respectively. It also has the lowest number of trainable parameters at 131,106 acquired from fifteen convolutional and depth-separable convolution layers.
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