# Exploring Disentangled Feature Representation Beyond Face Identification

arXiv: https://arxiv.org/abs/1804.03487 · HTML: https://mlsanity.com/paper/1804.03487/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 30 of 36)

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- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [FairCal: Fairness Calibration for Face Verification](https://mlsanity.com/paper/2106.03761/) (2021)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
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- [Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal](https://mlsanity.com/paper/2212.13023/) (2022)
- [DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning](https://mlsanity.com/paper/2109.12065/) (2021)
- [Remote Photoplethysmography in Real-World and Extreme Lighting Scenarios](https://mlsanity.com/paper/2503.11465/) (2025)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification](https://mlsanity.com/paper/2106.13929/) (2021)
- [StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification](https://mlsanity.com/paper/2405.12721/) (2024)
- [Disentangled Representations for Short-Term and Long-Term Person Re-Identification](https://mlsanity.com/paper/2409.05277/) (2024)
- [Learning Fair Face Representation With Progressive Cross Transformer](https://mlsanity.com/paper/2108.04983/) (2021)
- [Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition](https://mlsanity.com/paper/2404.10904/) (2024)
- [Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition](https://mlsanity.com/paper/2409.11652/) (2024)
- [Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision](https://mlsanity.com/paper/2103.03240/) (2021)
- [Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition](https://mlsanity.com/paper/2103.09233/) (2021)
- [Benchmarking Shadow Removal for Facial Landmark Detection and Beyond](https://mlsanity.com/paper/2111.13790/) (2021)
- [Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications](https://mlsanity.com/paper/2307.02881/) (2023)
- [Stochastic Block-ADMM for Training Deep Networks](https://mlsanity.com/paper/2105.00339/) (2021)
- [Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images](https://mlsanity.com/paper/2201.00100/) (2022)
- [Toward Fair Facial Expression Recognition with Improved Distribution Alignment](https://mlsanity.com/paper/2306.06696/) (2023)
- [L-VAE: Variational Auto-Encoder with Learnable Beta for Disentangled Representation](https://mlsanity.com/paper/2507.02619/) (2025)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning](https://mlsanity.com/paper/2301.05027/) (2023)
- [EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition](https://mlsanity.com/paper/2409.14432/) (2024)
- [Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal](https://mlsanity.com/paper/2106.01467/) (2021)
- [FaceCook: Face Generation Based on Linear Scaling Factors](https://mlsanity.com/paper/2109.03492/) (2021)

## Abstract (arXiv)

This paper proposes learning disentangled but complementary face features with minimal supervision by face identification. Specifically, we construct an identity Distilling and Dispelling Autoencoder (D2AE) framework that adversarially learns the identity-distilled features for identity verification and the identity-dispelled features to fool the verification system. Thanks to the design of two-stream cues, the learned disentangled features represent not only the identity or attribute but the complete input image. Comprehensive evaluations further demonstrate that the proposed features not only maintain state-of-the-art identity verification performance on LFW, but also acquire competitive discriminative power for face attribute recognition on CelebA and LFWA. Moreover, the proposed system is ready to semantically control the face generation/editing based on various identities and attributes in an unsupervised manner.