In this paper, we propose a cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions. We also propose a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework. The proposed method adopts a region-based technique in which face, eyes, nose and mouth regions are detected from the original face images. Multiple CNNs are used to extract deep features from each region, and then, a fusion network combines these features. Moreover, to provide user’s privacy and increase the system resistance against spoof attacks, a cancelable biometric technique using bio-convolving encryption is performed on the final facial descriptor. Our experiments on the FERET, LFW and PaSC datasets show excellent and competitive results compared to state-of-the-art methods in terms of recognition accuracy, specificity, precision, recall and f score .

中文翻译：

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基于深度学习的可取消多生物识别系统

在本文中，我们提出了一种可取消的多生物特征人脸识别方法，该方法使用多个卷积神经网络 (CNN) 从不同的面部区域中提取深层特征。我们还提出了一种新的 CNN 架构，该架构利用批量归一化、深度串联和残差学习框架。所提出的方法采用基于区域的技术，其中从原始人脸图像中检测人脸、眼睛、鼻子和嘴巴区域。使用多个 CNN 从每个区域提取深度特征，然后融合网络将这些特征组合在一起。此外，为了提供用户的隐私并增加系统对欺骗攻击的抵抗力，对最终的面部描述符执行使用生物卷积加密的可取消生物识别技术。我们在 FERET 上的实验，