标题： 基于质量引导的评分融合专家混合框架的人类识别 显示英文标题

标题： A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition

摘要： 全身生物特征识别是一项具有挑战性的多模态任务，它集成了各种生物特征模态，包括面部、步态和身体。 这种集成对于克服单一模态系统的局限性至关重要。 传统上，全身识别涉及部署不同的模型来处理多个模态，通过分数融合（例如，每个模型的相似性矩阵的加权平均）实现最终结果。 然而，这些传统方法可能会忽略单个模态的分数分布变化，使得提高最终性能变得困难。 在本工作中，我们提出了\textbf{Q}质量引导的\textbf{M}混合分数融合\textbf{E}专家（QME），这是一种新颖的框架，旨在通过使用专家混合（MoE）的可学习分数融合策略来提高全身生物特征识别性能。 我们引入了一种新的伪质量损失，用于通过模态特定的质量估计器（QE）进行质量估计，并引入了一个分数三元组损失以提高度量性能。 在多个全身生物特征数据集上的大量实验表明了我们所提出方法的有效性，在与基线方法相比的各种指标上取得了最先进的结果。 我们的方法适用于多模态和多模型，解决了相似性分数域中的模型错位和数据质量变化等关键挑战。 显示英文摘要

摘要： Whole-body biometric recognition is a challenging multimodal task that integrates various biometric modalities, including face, gait, and body. This integration is essential for overcoming the limitations of unimodal systems. Traditionally, whole-body recognition involves deploying different models to process multiple modalities, achieving the final outcome by score-fusion (e.g., weighted averaging of similarity matrices from each model). However, these conventional methods may overlook the variations in score distributions of individual modalities, making it challenging to improve final performance. In this work, we present \textbf{Q}uality-guided \textbf{M}ixture of score-fusion \textbf{E}xperts (QME), a novel framework designed for improving whole-body biometric recognition performance through a learnable score-fusion strategy using a Mixture of Experts (MoE). We introduce a novel pseudo-quality loss for quality estimation with a modality-specific Quality Estimator (QE), and a score triplet loss to improve the metric performance. Extensive experiments on multiple whole-body biometric datasets demonstrate the effectiveness of our proposed approach, achieving state-of-the-art results across various metrics compared to baseline methods. Our method is effective for multimodal and multi-model, addressing key challenges such as model misalignment in the similarity score domain and variability in data quality.