# Face Reconstruction from Face Embeddings using Adapter to a Face Foundation Model

arXiv: https://arxiv.org/abs/2411.03960 · HTML: https://mlsanity.com/paper/2411.03960/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [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)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Efficient Decision-based Black-box Adversarial Attacks on Face Recognition](https://mlsanity.com/paper/1904.04433/) (2019)
- [ElasticFace: Elastic Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/2109.09416/) (2021)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [AdvFaces: Adversarial Face Synthesis](https://mlsanity.com/paper/1908.05008/) (2019)
- [FaceX-Zoo: A PyTorch Toolbox for Face Recognition](https://mlsanity.com/paper/2101.04407/) (2021)
- [Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition](https://mlsanity.com/paper/2003.06958/) (2020)
- [Synthesizing Normalized Faces from Facial Identity Features](https://mlsanity.com/paper/1701.04851/) (2017)
- [MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron](https://mlsanity.com/paper/2204.11054/) (2022)

## Built on by (most cited first, 9 of 9)

- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space](https://mlsanity.com/paper/2506.09777/) (2025)
- [Semantic Leakage from Image Embeddings](https://mlsanity.com/paper/2601.22929/) (2026)
- [Realistic Face Reconstruction from Facial Embeddings via Diffusion Models](https://mlsanity.com/paper/2602.13168/) (2026)
- [LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction](https://mlsanity.com/paper/2604.18358/) (2026)
- [Signal-based Model Access Risk Analysis for AI System Operations Security](https://mlsanity.com/paper/2607.16414/) (2026)
- [SCOUT: Semantic Concept Discovery for Open-Vocabulary Editing of face Recognition Templates](https://mlsanity.com/paper/2608.16251/) (2026)
- [Breaking High Confidence: Practical Face Impersonation under High-Security Thresholds](https://mlsanity.com/paper/2608.20884/) (2026)
- [Unmasking Face Embeddings: Reading, Rendering and Naming with Foundation Models](https://mlsanity.com/paper/2609.00411/) (2026)

## Abstract (arXiv)

Face recognition systems extract embedding vectors from face images and use these embeddings to verify or identify individuals. Face reconstruction attack (also known as template inversion) refers to reconstructing face images from face embeddings and using the reconstructed face image to enter a face recognition system. In this paper, we propose to use a face foundation model to reconstruct face images from the embeddings of a blackbox face recognition model. The foundation model is trained with 42M images to generate face images from the facial embeddings of a fixed face recognition model. We propose to use an adapter to translate target embeddings into the embedding space of the foundation model. The generated images are evaluated on different face recognition models and different datasets, demonstrating the effectiveness of our method to translate embeddings of different face recognition models. We also evaluate the transferability of reconstructed face images when attacking different face recognition models. Our experimental results show that our reconstructed face images outperform previous reconstruction attacks against face recognition models.