Image quality is one of the most crucial influence factors when conducting biometric image-based human identification. However, it is not considered in most existing multi-biometric fusion algorithms. In this paper, a quality assessment-based dynamic weighted fusion algorithm is proposed, which applies a method of using image quality scores, and the scores are evaluated by integrating various image quality metrics to assess the quality of the feature matching process. According to the classification of the dual-modality feature matching quality, a dynamic weighted fusion strategy is proposed to increase the weight of biometric traits with better quality and weaken the impact of low-quality biometric traits on the recognition results, to achieve the adaptive optimization fusion of the biometric score level. Finally, the fused scores are used to make decisions. Experimental results reveal that the proposed algorithm is more robust and has better achievement than unimodal biometrics and traditional fusion algorithms.

图像质量是进行基于生物特征图像的人类识别时最关键的影响因素之一。但是，在大多数现有的多生物融合算法中都没有考虑它。本文提出了一种基于质量评估的动态加权融合算法，该算法应用了一种使用图像质量得分的方法，并通过集成各种图像质量指标对得分进行评估，以评估特征匹配过程的质量。根据双模态特征匹配质量的分类，提出一种动态加权融合策略，以增加质量较好的生物特征的权重，减弱低质量生物特征对识别结果的影响，实现自适应优化。生物统计得分水平的融合。最后，融合分数用于决策。实验结果表明，与单峰生物特征和传统融合算法相比，该算法具有更好的鲁棒性和更好的性能。