Cascaded multimodal biometric recognition framework
Cascaded multimodal biometric recognition framework
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DOI：
10.1049/iet-bmt.2012.0043
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
A. Baig;A. Bouridane;F. Kurugollu;B. Albesher
中科院分区：
文献类型：
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作者：
A. Baig;A. Bouridane;F. Kurugollu;B. Albesher
一个实用的多生物特征识别系统不仅应该稳定、鲁棒和准确，还应该遵守实时处理速度和内存限制。本研究提出了一种用于生物识别系统的基于级联分类器的框架。所提出的框架利用一组弱分类器来减少注册用户？数据集到一小部分候选用户。然后，强分类器集使用该列表作为级联的最后阶段来制定决策。在每个阶段，候选列表是通过基于马哈拉诺比斯距离的匹配分数质量度量生成的。作者框架的关键特征之一是，集合中的每个分类器都可以设计为使用不同的模态，从而提供真正的多模态生物特征识别系统的优势。此外，它是第一个真正的基于多模式级联分类器的生物特征识别方法之一。对单模态和多模态所提出的系统的性能进行了评估，以证明该方法的有效性。
A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users?? dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.