Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption

Abstract Face recognition is playing an increasingly important role in present society, and suffers from the privacy leakage in plaintext. Therefore, a recognition system based on homomorphic encryption that supports privacy preservation is designed and implemented in this paper. This system uses the CKKS algorithm in the SEAL library, Microsoft’s latest homomorphic encryption achievement, to encrypt the normalized face feature vectors, and uses the FaceNet neural network to learn on the image’s ciphertext to achieve face classification. Finally, face recognition in ciphertext is accomplished. After been tested, the whole process of extracting feature vectors and encrypting a face image takes only about 1.712s in the developed system. The average time to compare a group of images in ciphertext is about 2.06s, and a group of images can be effectively recognized within 30 degrees of face bias, the identification accuracy can reach 96.71%. Compared with the face recognition scheme based on the Advanced Encryption Standard(AES) encryption algorithm in ciphertext proposed by Wang et al. in 2019, our scheme improves the recognition accuracy by 4.21%. Compared with the image recognition scheme based on Elliptical encryption algorithm in ciphertext proposed by Kumar S et al. in 2018, the total time in our system is decreased by 76.2%. Therefore, this scheme has better operational efficiency and practical value while ensuring the users’ personal privacy. Compared with the face recognition system in plaintext presented in recent years, our scheme has almost the same level on recognition accuracy and time efficiency.