Abstract
Multimodal large language models (MLLMs) have achieved remarkable performance across diverse vision-and-language tasks. However, their potential in face recognition remains underexplored. In particular, the performance of open-source MLLMs needs to be evaluated and compared with existing face recognition models on standard benchmarks with similar protocol. In this work, we present a systematic benchmark of state-of-the-art MLLMs for face recognition on several face recognition datasets, including LFW, CALFW, CPLFW, CFP, AgeDB and RFW. Experimental results reveal that while MLLMs capture rich semantic cues useful for face-related tasks, they lag behind specialized models in high-precision recognition scenarios in zero-shot applications. This benchmark provides a foundation for advancing MLLM-based face recognition, offering insights for the design of next-generation models with higher accuracy and generalization. The source code of our benchmark is publicly available in the project page.
Abstract (translated)
多模态大型语言模型（MLLM）在多种视觉和语言任务中取得了显著的性能。然而，它们在人脸识别领域的潜力尚未得到充分探索。特别是，开源的多模态大模型需要在其与现有标准基准测试相匹配的情况下进行评估，并与其他面部识别模型进行比较。在这项工作中，我们对几种面部识别数据集（包括LFW、CALFW、CPLFW、CFP、AgeDB和RFW）上最先进的MLLM进行了系统性的基准测试。实验结果表明，虽然多模态大模型能够捕捉到用于人脸相关任务的丰富语义线索，但在零样本应用中的高精度识别场景中，它们仍然落后于专门的人脸识别模型。这项基准测试为推进基于多模态大型语言模型的人脸识别奠定了基础，并为下一代更高准确性和泛化能力的模型设计提供了见解。我们的基准测试源代码已在项目页面上公开提供。
URL
https://arxiv.org/abs/2510.14866
