# Explainable Face Verification via Feature-Guided Gradient Backpropagation

arXiv: https://arxiv.org/abs/2403.04549 · HTML: https://mlsanity.com/paper/2403.04549/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Real Time Image Saliency for Black Box Classifiers](https://mlsanity.com/paper/1705.07857/) (2017)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers](https://mlsanity.com/paper/1604.00825/) (2016)
- [MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices](https://mlsanity.com/paper/1804.07573/) (2018)
- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [Black-box Explanation of Object Detectors via Saliency Maps](https://mlsanity.com/paper/2006.03204/) (2020)
- [Explainable Face Recognition](https://mlsanity.com/paper/2008.00916/) (2020)
- [xCos: An Explainable Cosine Metric for Face Verification Task](https://mlsanity.com/paper/2003.05383/) (2020)
- [Explainable Model-Agnostic Similarity and Confidence in Face Verification](https://mlsanity.com/paper/2211.13735/) (2022)
- [Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation](https://mlsanity.com/paper/2304.13409/) (2023)
- [Towards Visual Saliency Explanations of Face Verification](https://mlsanity.com/paper/2305.08546/) (2023)
- [When Face Recognition Meets Occlusion: A New Benchmark](https://mlsanity.com/paper/2103.02805/) (2021)
- [Discriminative Deep Feature Visualization for Explainable Face Recognition](https://mlsanity.com/paper/2306.00402/) (2023)

## Built on by (most cited first, 6 of 6)

- [Beyond Spatial Explanations: Explainable Face Recognition in the Frequency Domain](https://mlsanity.com/paper/2407.11941/) (2024)
- [MLLM-based Textual Explanations for Face Comparison](https://mlsanity.com/paper/2603.16629/) (2026)
- [Towards A Comprehensive Visual Saliency Explanation Framework for AI-based Face Recognition Systems](https://mlsanity.com/paper/2407.05983/) (2024)
- [Pose-invariant face recognition via feature-space pose frontalization](https://mlsanity.com/paper/2505.16412/) (2025)
- [FAME: Feature Activation Map Explanation on Image Classification and Face Recognition](https://mlsanity.com/paper/2605.12017/) (2026)
- [Vision Language Model Fusion for Explainable Face Recognition](https://mlsanity.com/paper/2608.24430/) (2026)

## Abstract (arXiv)

Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such systems. Existing studies relying on various mechanisms have investigated the usage of saliency maps as an explanation approach, but suffer from different limitations. This paper first explores the spatial relationship between face image and its deep representation via gradient backpropagation. Then a new explanation approach FGGB has been conceived, which provides precise and insightful similarity and dissimilarity saliency maps to explain the "Accept" and "Reject" decision of an FR system. Extensive visual presentation and quantitative measurement have shown that FGGB achieves superior performance in both similarity and dissimilarity maps when compared to current state-of-the-art explainable face verification approaches.