TY - Jour A2 - Anees，Amir Au - 尤拉，Asad Au - Wang，Jing Au - Anwar，M. Shahid Au - Ahmad，Arshad Au - Nazir，Shah Au - Khan，Habib Ullah Au - Fei，Zesong Py - 2021- 2021/01/30 - 安全面部表情识别的机器学习和隐私保留SP - 6673992 VL - 2021 AB - 由于其实际和潜在的应用，面部表情识别（FER）的兴趣日益增加，如人体生理相互作用诊断和精神疾病检测。该地区近年来从研究界受到了很多关注，并取得了显着的结果;然而，空间问题需要显着改善。本研究工作提出了一种新颖的框架，并为不受约束环境下的FER提出了有效和强大的解决方案;它还有助于我们在客户端/服务器模型中对面部图像以及保留隐私进行分类。有很多加密技术可用，但它们是计算昂贵的;在另一边，我们已经实现了一种能够在随机化的帮助下确保安全通信的轻量级方法。最初，我们执行预处理技术来遇到无约束的环境。 Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques. SN - 1939-0114 UR - https://doi.org/10.1155/2021/6673992 DO - 10.1155/2021/6673992 JF - Security and Communication Networks PB - Hindawi KW - ER -