Single sensor-based multi-modal biometrics is a promising approach that offers simple system construction, low cost, and wide applicability to real situations such as CCTV footage-based criminal investigations. In multi-modal biometrics, fusion at the score-level is a popular and promising approach, and data qualities that affect the matching score of each modality are often incorporated as a quality-dependent score-level fusion framework. This paper presents a very large-scale single sensor-based multi-quality multi-modal biometric score database called MultiQ Score Database version 2 to advance the research into evaluation, comparison, and benchmarking of score-level fusion approaches using both quality-independent and quality-dependent protocols. We extracted gait, head, and height modalities from the OU-ISIR Gait Database and introduce spatial resolution (SR), temporal resolution (TR) and view as quality measures that significantly affect biometric system performance. We considered seven and 10 scaling factors for SR and TR, respectively, with four view variations. We then constructed a database comprising approximately 4 million genuine and 7.5 billion imposter score databases. To evaluate this database, we set two different protocols, and provided a set recognition accuracy for state-of-the-art approaches using protocols for both quality-independent and quality-dependent schemes. This database and the evaluation results will be beneficial for score-level fusion research. Additionally, we provide detailed analysis of the recognition accuracies associated with gait, head, and height modalities in different spatial/temporal resolutions and views. These analyses may be useful in criminal investigation research.

中文翻译：

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MultiQ：基于单传感器的多质量多模式大规模生物特征评分数据库及其性能评估

基于单个传感器的多模式生物特征识别技术是一种很有前途的方法，它可以提供简单的系统构建，低成本以及对诸如基于CCTV录像的犯罪调查等实际情况的广泛适用性。在多模式生物特征学中，分数级别的融合是一种流行且有前途的方法，并且影响每种方式的匹配分数的数据质量通常作为质量依赖的分数级别融合框架而并入。本文介绍了一个非常大规模的基于单传感器的多质量多模式生物特征得分数据库，称为MultiQ得分数据库版本2，以将研究发展到评估，比较和基准测试中，同时使用质量无关和质量无关的得分级别融合方法质量相关的协议。我们提取了步态，头部，和OU-ISIR步态数据库中的高度模式，并介绍了空间分辨率（SR），时间分辨率（TR）和视图，它们是对生物识别系统性能产生重大影响的质量度量。我们分别考虑了SR和TR的七个和10个缩放因子，以及四个视图变化。然后，我们构建了一个包含约400万个真实和75亿冒名顶替者分数数据库的数据库。为了评估该数据库，我们设置了两种不同的协议，并为使用质量无关和质量依赖方案的协议的最新方法提供了一组识别精度。该数据库和评估结果将有助于分数级融合研究。此外，我们还会详细分析与步态，头部，和不同高度的空间/时间分辨率和视图的高度模式。这些分析可能在刑事调查研究中很有用。