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

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

URL: http://arxiv.org/abs/2508.00053v1
Date: Thu, 31 Jul 2025 18:00:01 GMT
Title: A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition
Authors: Jie Zhu, Yiyang Su, Minchul Kim, Anil Jain, Xiaoming Liu,
Abstract summary: Whole-body biometric recognition is a challenging task that integrates various biometric modalities.<n>We present textbfQuality-guided textbfMixture of score-fusion textbfExperts (QME)<n>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.
Score: 14.242680363313148
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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