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首页> 外文期刊>Multimedia Tools and Applications >A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition
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A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition

机译：基于默认的Xceptio-Inception和Xception-Rexing模块构建了Lite卷积神经网络，用于基于纹理的面部活力识别

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摘要

Face recognition is one of the emerging areas in the field of biometric and computer vision that plays an important role in numerous time-bound applications such as ATM payment, criminal identification, E-Learning, healthcare, and online gaming. It can be compromised by various imposter attacks such as masks, print, or replay attacks. So, there is a requirement of a light-weight powerful classifier that could take significantly less time to minimize those effects by observing the liveness of a current person. In this paper, a lightweight permuted Xceptio-Inception/Reduction Convolutional Neural Network classifier has been proposed using depthwise convolution, permutation, reshape, and residual techniques for texture-based facial liveness recognition. It has been validated with moderately dense ImageNet benchmarked Convolutional Neural Network classifiers with respect to weight size, accuracy, precision, and recall. Here, we have considered some of the variants of most popular convolution neural networks such as AlexNet, Inception, ResNet, and VGGNet and applied these models for textured based facial liveness recognition. Before the training and testing of those classifiers, all the frontal face images from the FRAUD2, NUAA, and CASIA FASD imposter datasets had normalized, and the multi-colored space LBP feature maps extracted from these normalized image frames had supplied as inputs to the classifiers. The results show that the proposed convolutional neural network performs best among the above-standardized network models, whose total weights consumes less memory space, which leads to fast liveness face recognition. In the end, comparison with the previous work shows that it achieves almost the highest success rate and lowest Equal Error Rate as a non-intrusive classifier.
机译：面部识别是生物识别和计算机视野领域的新兴区域之一，在诸如ATM支付，刑事识别，电子学习，医疗保健和在线游戏之类的许多时限应用中起着重要作用。它可能因各种冒犯攻击而受到损害，例如掩码，打印或重放攻击。因此，需要一种轻量级强大的分类器，可以通过观察当前人的活力来最小化这些效果的时间明显更少。本文使用了基于纹理的面部活力识别的深度卷积，置换，置换，置换，折叠和残差技术，提出了一种轻量级允许的Xception-Inception / Reply卷积神经网络分类器。它已经通过适度密集的Imageenet基准测试卷积神经网络分类器，重量尺寸，准确性，精度和召回。在这里，我们考虑了一些最受欢迎的卷积神经网络的一些变体，例如AlexNet，Inception，Reset和Vggnet，并应用了基于纹理的面部活力识别的这些模型。在培训和测试这些分类器之前，来自欺诈2，NUA和CASIA FASD冒险数据集的所有正面图像都有标准化，并且从这些归一化图像帧中提取的多彩色空间LBP特征映射已作为输入到分类器的输入提供。结果表明，所提出的卷积神经网络在上述标准化网络模型中表现最佳，其总重量消耗更少的内存空间，这导致快速的情绪面部识别。最后，与前一项工作的比较表明，它几乎实现了最高的成功率和最低的相同错误率作为非侵入式分类器。

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