VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimation

• URL: http://arxiv.org/abs/2603.26015v1
• Date: Fri, 27 Mar 2026 02:16:22 GMT
• Title: VLAgeBench: Benchmarking Large Vision-Language Models for Zero-Shot Human Age Estimation
• Abstract summary: This study presents a comprehensive zero-shot evaluation of large vision-language models (LVLMs) for facial age estimation.<n>We demonstrate that general-purpose LVLMs can deliver competitive performance in zero-shot settings.<n>This work positions LVLMs as promising tools for real-world applications in forensic science, healthcare monitoring, and human-computer interaction.
• Score: 0.19573380763700718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human age estimation from facial images represents a challenging computer vision task with significant applications in biometrics, healthcare, and human-computer interaction. While traditional deep learning approaches require extensive labeled datasets and domain-specific training, recent advances in large vision-language models (LVLMs) offer the potential for zero-shot age estimation. This study presents a comprehensive zero-shot evaluation of state-of-the-art Large Vision-Language Models (LVLMs) for facial age estimation, a task traditionally dominated by domain-specific convolutional networks and supervised learning. We assess the performance of GPT-4o, Claude 3.5 Sonnet, and LLaMA 3.2 Vision on two benchmark datasets, UTKFace and FG-NET, without any fine-tuning or task-specific adaptation. Using eight evaluation metrics, including MAE, MSE, RMSE, MAPE, MBE, $R^2$, CCC, and $\pm$5-year accuracy, we demonstrate that general-purpose LVLMs can deliver competitive performance in zero-shot settings. Our findings highlight the emergent capabilities of LVLMs for accurate biometric age estimation and position these models as promising tools for real-world applications. Additionally, we highlight performance disparities linked to image quality and demographic subgroups, underscoring the need for fairness-aware multimodal inference. This work introduces a reproducible benchmark and positions LVLMs as promising tools for real-world applications in forensic science, healthcare monitoring, and human-computer interaction. The benchmark focuses on strict zero-shot inference without fine-tuning and highlights remaining challenges related to prompt sensitivity, interpretability, computational cost, and demographic fairness.

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