Kothadiya Deep, Bhatt Chintan, Soni Dhruvil, Gadhe Kalpita, Patel Samir, Bruno Alessandro, Mazzeo Pier Luigi
U & P U Patel Department of Computer Engineering, CHA-RUSAT Campus, Charotar University of Science and Technology, Petlad 388421, India.
Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India.
J Imaging. 2023 Aug 7;9(8):158. doi: 10.3390/jimaging9080158.
Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method's performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.
指纹图像的活体检测在有效防止任何未经授权的活动或网络钓鱼企图方面发挥着作用。独特的个人身份识别的可及性提高了生物识别技术的普及程度。基于计算机视觉的深度学习在图像分类、检测及许多其他方面都取得了显著成果。所提出的方法依赖于注意力模型和残差网络（ResNet）卷积。空间注意力（SA）模型和通道注意力（CA）模型被依次使用以增强特征学习。一个三重顺序注意力模型与五个卷积学习层一起使用。该方法的性能在LivDet - 2021数据集上针对不同的池化策略（如最大池化、平均池化和随机池化）进行了测试。与卷积神经网络的不同先进变体（如DenseNet121、VGG19、InceptionV3和传统的ResNet50）进行了比较。特别是，测试旨在通过进一步增强顺序注意力模型来评估ResNet34和ResNet50模型在特征提取方面的性能。一个与全连接层一起使用的多层感知器（MLP）分类器返回整个网络的最终预测结果。最后，还针对有和没有注意力模型的ResNet在特征提取方面以及考虑不同池化策略的情况对所提出的方法进行了评估。
