多模态大语言模型在人脸识别中的基准评测
Benchmarking Multimodal Large Language Models for Face Recognition
AI总结：
本文系统评测了多模态大语言模型在多个标准人脸识别数据集上的性能，发现其虽能捕获丰富语义线索，但在零样本高精度识别场景中落后于专用模型，并为下一代模型设计提供了见解。
摘要：
多模态大语言模型（MLLMs）在各种视觉与语言任务中已取得显著性能。然而，它们在人脸识别方面的潜力仍未得到充分探索。特别是，开源多模态大语言模型的性能需要在具有相似协议的标准基准上，与现有人脸识别模型进行评估和比较。在这项工作中，我们在多个人脸识别数据集（包括LFW、CALFW、CPLFW、CFP、AgeDB和RFW）上，对最先进的多模态大语言模型进行了系统性基准评测。实验结果表明，虽然多模态大语言模型捕获了丰富的语义线索，有助于人脸相关任务，但在零样本应用的高精度识别场景中，它们落后于专用模型。该基准为推进基于多模态大语言模型的人脸识别奠定了基础，为设计具有更高准确性和泛化能力的下一代模型提供了见解。我们基准的源代码已在项目页面公开提供。
英文摘要：
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.
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