# Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain

arXiv: https://arxiv.org/abs/2401.13386 · HTML: https://mlsanity.com/paper/2401.13386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Quantization Guided JPEG Artifact Correction](https://mlsanity.com/paper/2004.09320/) (2020)
- [Synthesizing Normalized Faces from Facial Identity Features](https://mlsanity.com/paper/1701.04851/) (2017)
- [Privacy Preserving Face Recognition Utilizing Differential Privacy](https://mlsanity.com/paper/2005.10486/) (2020)
- [Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain](https://mlsanity.com/paper/2207.07316/) (2022)
- [Privacy-Preserving Face Recognition Using Random Frequency Components](https://mlsanity.com/paper/2308.10461/) (2023)
- [Towards Protecting Face Embeddings in Mobile Face Verification Scenarios](https://mlsanity.com/paper/2110.00434/) (2021)
- [Exploring Semantic Segmentation on the DCT Representation](https://mlsanity.com/paper/1907.10015/) (2019)
- [Canonical Face Embeddings](https://mlsanity.com/paper/2106.07822/) (2021)
- [Color-related Local Binary Pattern: A Learned Local Descriptor for Color Image Recognition](https://mlsanity.com/paper/2012.06132/) (2020)

## Built on by (most cited first, 2 of 2)

- [Robust Skin Color Driven Privacy Preserving Face Recognition via Function Secret Sharing](https://mlsanity.com/paper/2407.05045/) (2024)
- [Realistic Face Reconstruction from Facial Embeddings via Diffusion Models](https://mlsanity.com/paper/2602.13168/) (2026)

## Abstract (arXiv)

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.