Secure and efficient outsourcing of PCA-based face recognition

Face recognition has become increasingly popular in recent years. However, in some special cases, many face recognition calculations cannot be performed effectively due to the lack of sufficient computing power of the terminal, which poses a challenge to the practical application of face recognition technology. Cloud computing provides a good platform for solving this problem due to its abundant computing resources. However, cloud computing poses new challenges, such as how to protect clients' data privacy without reducing efficiency. In this paper, we review some of the results of previous research and analyze an outsourcing protocol for eigen decomposition and singular value decomposition. On this basis, we propose a secure and efficient outsourcing protocol for face recognition through principal component analysis. In the proposed protocol, information privacy is well protected, and computational resources are saved by means of conversions of the original image information. In addition, local verification is supported to cope with the laziness of the cloud. We show the feasibility and advancement of our protocol from both theoretical and experimental perspectives.