# Towards Interpretable Face Recognition

arXiv: https://arxiv.org/abs/1805.00611 · HTML: https://mlsanity.com/paper/1805.00611/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

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

- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [Towards Visual Saliency Explanations of Face Verification](https://mlsanity.com/paper/2305.08546/) (2023)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [ReliableSwap: Boosting General Face Swapping Via Reliable Supervision](https://mlsanity.com/paper/2306.05356/) (2023)
- [3D Face Reconstruction: the Road to Forensics](https://mlsanity.com/paper/2309.11357/) (2023)
- [Locality-aware Channel-wise Dropout for Occluded Face Recognition](https://mlsanity.com/paper/2107.09270/) (2021)
- [Interpreting Face Inference Models using Hierarchical Network Dissection](https://mlsanity.com/paper/2108.10360/) (2021)
- [3D Face Reconstruction for Forensic Recognition -- A Survey](https://mlsanity.com/paper/2303.11164/) (2023)
- [An Explainable Model-Agnostic Algorithm for CNN-based Biometrics Verification](https://mlsanity.com/paper/2307.13428/) (2023)
- [Attributes Shape the Embedding Space of Face Recognition Models](https://mlsanity.com/paper/2507.11372/) (2025)
- [Deep face recognition with clustering based domain adaptation](https://mlsanity.com/paper/2205.13937/) (2022)
- [Explanation of Face Recognition via Saliency Maps](https://mlsanity.com/paper/2304.06118/) (2023)
- [Discriminative Deep Feature Visualization for Explainable Face Recognition](https://mlsanity.com/paper/2306.00402/) (2023)
- [Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN](https://mlsanity.com/paper/2201.09689/) (2022)
- [Activation Template Matching Loss for Explainable Face Recognition](https://mlsanity.com/paper/2207.02179/) (2022)
- [GiMeFive: Towards Interpretable Facial Emotion Classification](https://mlsanity.com/paper/2402.15662/) (2024)
- [Bridging Human Concepts and Computer Vision for Explainable Face Verification](https://mlsanity.com/paper/2403.08789/) (2024)
- [A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2503.06451/) (2025)
- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [Adaptive Face Recognition Using Adversarial Information Network](https://mlsanity.com/paper/2305.13605/) (2023)
- [Towards A Comprehensive Visual Saliency Explanation Framework for AI-based Face Recognition Systems](https://mlsanity.com/paper/2407.05983/) (2024)
- [Explaining Deep Face Algorithms through Visualization: A Survey](https://mlsanity.com/paper/2309.14715/) (2023)
- [AttriBE: Quantifying Attribute Expressivity in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2604.27218/) (2026)
- [AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations](https://mlsanity.com/paper/2608.14130/) (2026)

## Abstract (arXiv)

Deep CNNs have been pushing the frontier of visual recognition over past years. Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions. Recent works further push the interpretability in the network learning stage to learn more meaningful representations. In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition. We propose a spatial activation diversity loss to learn more structured face representations. By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions. We demonstrate on three face recognition benchmarks that our proposed method is able to improve face recognition accuracy with easily interpretable face representations.