A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition
Zhu, Jie Su, Yiyang Kim, Minchul Jain, Anil Liu, Xiaoming
Zhu, Jie
Su, Yiyang
Kim, Minchul
Jain, Anil
Liu, Xiaoming
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Department
Computer Vision
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Conference proceeding
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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 Quality-guided Mixture of score-fusion Experts (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 $(Q E)$, 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. Code is available at the Project Link.
Citation
J. Zhu, Y. Su, M. Kim, A. Jain, X. Liu, "A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition," 2026, pp. 13076-13086.
Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Conference
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Keywords
46 Information and Computing Sciences, 4603 Computer Vision and Multimedia Computation, 4605 Data Management and Data Science
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Source
2025 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE