  Face recognition technology has been deployed in various real-life
applications. The most sophisticated deep learning-based face recognition
systems rely on training millions of face images through complex deep neural
networks to achieve high accuracy. It is quite common for clients to upload
face images to the service provider in order to access the model inference.
However, the face image is a type of sensitive biometric attribute tied to the
identity information of each user. Directly exposing the raw face image to the
service provider poses a threat to the user's privacy. Current
privacy-preserving approaches to face recognition focus on either concealing
visual information on model input or protecting model output face embedding.
The noticeable drop in recognition accuracy is a pitfall for most methods. This
paper proposes a hybrid frequency-color fusion approach to reduce the input
dimensionality of face recognition in the frequency domain. Moreover, sparse
color information is also introduced to alleviate significant accuracy
degradation after adding differential privacy noise. Besides, an
identity-specific embedding mapping scheme is applied to protect original face
embedding by enlarging the distance among identities. Lastly, secure multiparty
computation is implemented for safely computing the embedding distance during
model inference. The proposed method performs well on multiple widely used
verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy
than the state-of-the-art in the 1:N verification scenario.
