Privacy-preserving face recognition method based on extensible feature extraction

Face recognition (FR) technique has become a pervasive and ubiquitous part of daily lives, from unlocking our smartphones with a glance to being scanned by surveillance cameras in various outdoor locations. When people’s face photos are uploaded to the cloud for face recognition processing, they often have legitimate concerns about the privacy and security of their biometric data. A number of privacy-preserving face recognition (PPFR) frameworks have been proposed to address these issues by enabling the cloud to perform face recognition without revealing the identity or features of the face photos. However, these frameworks suffer from several limitations. They rely on computationally intensive operations that increase the cost and time of face recognition, leading to less applications in the real-world scenario. Many current frameworks support only one face recognition method and cannot be extended to different models. To overcome these challenges, in this paper, we propose a PPFR framework with high recognition accuracy based on extensible feature extraction for different application scenarios. In particular, features are extracted by a selective model, such as MobileFaceNet, ResNet-18 or ResNet-50, and encrypted by a randomness-based encryption algorithm in both face owner and user. Cloud service provider (SP) performs face recognition by comparing the Euclidean distances between features received from the above two entities. Extensive experiments verify that the proposed framework has significant advantages in terms of accuracy and efficiency.