Automatic facial recognition is fast becoming a reliable method for identifying individuals. Due to its reliability and unobtrusive nature facial recognition has been widely deployed in law enforcement and civilian application. Recent implementations of facial recognition systems on public cloud computing infrastructures have raised strong concerns regarding an individual's privacy. In this paper, we propose and implement a novel approach for facial recognition in the encrypted domain. This allows for facial recognition to be performed without revealing the actual image unnecessarily as the features stay encrypted at all times. Our proposed system exploits the homomorphic properties of the Paillier cryptosystem and performs Euclidean distance calculations using encrypted data. We propose to represent the images using a radial Local Ternary Pattern approach where a higher than proposed radius is used to extract the image features. Our proposed system has been evaluated using two publicly available datasets and has also been compared against the previously used eigenface approach in the encrypted domain and the obtained results justify the feasibility of the proposed system.

自动面部识别正迅速成为一种用于识别个人的可靠方法。由于其可靠性和不显眼的性质，面部识别已广泛应用于执法和民用领域。人脸识别系统在公共云计算基础架构上的最新实现引起了人们对个人隐私的强烈关注。在本文中，我们提出并实现了一种在加密域中进行人脸识别的新方法。由于特征始终保持加密状态，因此可以进行面部识别而无需不必要地透露实际图像。我们提出的系统利用了Paillier密码系统的同态性质，并使用加密的数据执行欧几里得距离计算。我们建议使用径向局部三元模式方法来表示图像，其中使用比建议的半径高的半径来提取图像特征。我们的拟议系统已使用两个公开可用的数据集进行了评估，并且已与加密域中以前使用的特征面方法进行了比较，获得的结果证明了拟议系统的可行性。