Abstract: To address the conflict between privacy protection and recognition performance in facial recognition technology, this paper proposes a facial privacy-preserving recognition method combining generator and embedding networks. The method first uses a convolutional neural network-based generator model to apply random perturbations to the pixel values of facial images, generating distorted facial images that are imperceptible to the human eye but recognizable by specific deep neural networks, thus forming cancelable facial templates. Then, features are extracted using a pre-trained FaceNet embedding network model for recognition. During the training process, to ensure the recognizability of the facial templates, a residual structure is employed in the generator network model to effectively extract key features from the original image, enhancing the image’s expressiveness and reducing information loss to some extent. To increase the difference between the original image and the generated image and improve the diversity of the generated images, a generative hybrid loss function and a diversity loss function are introduced. To improve recognition accuracy, an improved triplet loss function is used to optimize the model. Experimental results show that this method not only enhances the privacy security of facial templates but also strengthens the diversity between generated images, improving the model's robustness through the diversity loss function. Experiments on the Aberdeen, GT, and LFW datasets demonstrate that the improved triplet loss function achieves more representative feature representations in the cosine embedding space, with recognition accuracies reaching 99.87%, 99.29%, and 98.59%, respectively.

摘要： 为解决人脸识别技术中隐私保护与识别性能间的矛盾，在这里提出了一种结合生成和嵌入网络的人脸隐私保护识别方法。该方法首先利用卷积神经网络生成模型对人脸图像像素值进行随机扰动，生成无法被人类视觉识别但可由特定深度神经网络识别的失真人脸图像，形成可撤销人脸模板。随后，通过预训练的FaceNet嵌入网络模型提取特征进行识别。在训练过程中，为保证人脸模板的可识别性，在生成网络模型中利用残差结构，从原始图像中有效地提取关键特征，增强图像的表现力，并在一定程度上减少信息丢失。为增大原始图像和生成图像之间的差异和提高生成图像的多样性，引入了生成混合损失函数和多样性损失函数。为提高识别准确率，利用改进的三元损失函数来优化模型。研究结果显示，该方法不仅提升了人脸模板的隐私安全性，还通过多样性损失函数增强了生成图像间的差异性，提高了模型鲁棒性。在Aberdeen、GT和LFW数据集上的实验表明，改进的三元损失函数在余弦嵌入空间中获得了更具代表性的特征表示，识别准确率分别达到了99.87%、99.29%和98.59%。