# FaceXBench: Evaluating Multimodal LLMs on Face Understanding

arXiv: https://arxiv.org/abs/2501.10360 · HTML: https://mlsanity.com/paper/2501.10360/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Social bias and fairness in VLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 86 of its 113 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Social bias and fairness in VLMs](https://mlsanity.com/mllm/social-bias-and-fairness-in-vlms/)

## Builds on (most cited first, 30 of 86)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [Chameleon: Mixed-Modal Early-Fusion Foundation Models](https://mlsanity.com/paper/2405.09818/) (2024)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)

## Built on by (most cited first, 24 of 24)

- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [FaceLLM: A Multimodal Large Language Model for Face Understanding](https://mlsanity.com/paper/2507.10300/) (2025)
- [Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition](https://mlsanity.com/paper/2507.03541/) (2025)
- [AvatarShield: Visual Reinforcement Learning for Human-Centric Synthetic Video Detection](https://mlsanity.com/paper/2505.15173/) (2025)
- [Benchmarking Multimodal Large Language Models for Face Recognition](https://mlsanity.com/paper/2510.14866/) (2025)
- [FaceInsight: A Multimodal Large Language Model for Face Perception](https://mlsanity.com/paper/2504.15624/) (2025)
- [Evaluating Multimodal Large Language Models for Heterogeneous Face Recognition](https://mlsanity.com/paper/2601.15406/) (2026)
- [MLLM-based Textual Explanations for Face Comparison](https://mlsanity.com/paper/2603.16629/) (2026)
- [M-PACE: Mother Child Framework for Multimodal Compliance](https://mlsanity.com/paper/2509.15241/) (2025)
- [FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis](https://mlsanity.com/paper/2512.18073/) (2025)
- [Focal-RegionFace: Generating Fine-Grained Multi-attribute Descriptions for Arbitrarily Selected Face Focal Regions](https://mlsanity.com/paper/2601.00156/) (2026)
- [Emergent Morphing Attack Detection in Open Multi-modal Large Language Models](https://mlsanity.com/paper/2602.15461/) (2026)
- [Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification](https://mlsanity.com/paper/2603.25613/) (2026)
- [In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems](https://mlsanity.com/paper/2507.15285/) (2025)
- [Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions](https://mlsanity.com/paper/2507.21015/) (2025)
- [Reliable and Reproducible Demographic Inference for Fairness in Face Analysis](https://mlsanity.com/paper/2510.20482/) (2025)
- [Demographic and Linguistic Bias Evaluation in Omnimodal Language Models](https://mlsanity.com/paper/2604.10014/) (2026)
- [FaceMoE: Mixture of Experts for Low-Resolution Face Recognition](https://mlsanity.com/paper/2606.32040/) (2026)
- [SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification](https://mlsanity.com/paper/2607.15517/) (2026)
- [3D FaceShell: Attribute Transfer in 3D Face Avatars as a VLM Defense Mechanism](https://mlsanity.com/paper/2607.16280/) (2026)
- [Vision Language Model Fusion for Explainable Face Recognition](https://mlsanity.com/paper/2608.24430/) (2026)
- [Beauty is in the AI of the beholder: MLLMs systematically overrate facial attractiveness](https://mlsanity.com/paper/2609.02512/) (2026)
- [Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition](https://mlsanity.com/paper/2609.21879/) (2026)
- [Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation](https://mlsanity.com/paper/2610.06102/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) demonstrate impressive problem-solving abilities across a wide range of tasks and domains. However, their capacity for face understanding has not been systematically studied. To address this gap, we introduce FaceXBench, a comprehensive benchmark designed to evaluate MLLMs on complex face understanding tasks. FaceXBench includes 5,000 multimodal multiple-choice questions derived from 25 public datasets and a newly created dataset, FaceXAPI. These questions cover 14 tasks across 6 broad categories, assessing MLLMs' face understanding abilities in bias and fairness, face authentication, recognition, analysis, localization and tool retrieval. Using FaceXBench, we conduct an extensive evaluation of 26 open-source MLLMs alongside 2 proprietary models, revealing the unique challenges in complex face understanding tasks. We analyze the models across three evaluation settings: zero-shot, in-context task description, and chain-of-thought prompting. Our detailed analysis reveals that current MLLMs, including advanced models like GPT-4o, and GeminiPro 1.5, show significant room for improvement. We believe FaceXBench will be a crucial resource for developing MLLMs equipped to perform sophisticated face understanding. Code: https://github.com/Kartik-3004/facexbench