Adaptive multi-classifier systems for biometric recognition

用于生物特征识别的自适应多分类器系统

• 批准号: 312451-2011
• 负责人: Granger, Éric
• 金额: $ 1.6万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=481273
• 关键词: Adaptive; multi; classifier; systems; biometric

Biometric recognition of individuals provides a powerful alternative to traditional authentication schemes that are presently applied in many security and surveillance systems. In practice, the performance of biometric systems typically declines because they face complex environments that change during operations, and they are designed a priori using limited data and knowledge of underlying data distributions. Biometric models are often poor representatives of the biometric trait to be recognized. For accurate recognition, these models should be adapted over time in response to new or changing input features, data samples, priors, classes and environments. This research program seeks to investigate adaptive multi-classifier systems (AMCSs) that can achieve a high level of performance in real-world biometric applications, and efficiently update biometric models in response to emerging information from the operational environment. These AMCSs evolve an ensemble of binary classifiers (EoCs) per individual, where classifiers are co-jointly trained using population-based evolutionary optimization. During the enrolment of an individual to system, a new dynamic multi-objective PSO-based training strategy generates a diversified pool of base classifiers through batch learning of data samples. Then, in response to new data for that individual, this strategy either generates an additional pool for combination with previously-learned classifiers, or evolves the pool of previously-learned classifiers through incremental learning. A subset of classifiers is then selected from an individual's pool according to specialized measures of accuracy and diversity. New incremental Boolean combination techniques are employed to adapt decision-level fusion functions over time, in response to new or changing pools. To account for limited data and skewed distributions, incremental BC is applied in ROC or other spaces. Although the robust adaptive techniques described in this proposal can be applied to a wide range of applications, face recognition, signature verification and biometric fusion are the focus of this research. To accelerate all steps of this program, new AMCSs will be validated with real biometric data on high-speed GPGPU platforms.

对个体的生物识别识别为传统身份验证方案提供了有力的替代方法，这些方案目前应用于许多安全和监视系统。在实践中，生物识别系统的性能通常会下降，因为它们面临着在操作过程中变化的复杂环境，并且使用有限的数据和基础数据分布的知识设计了先验。生物识别模型通常是要识别的生物特征性状的差代表。为了准确识别，应随着时间的推移对这些模型进行调整，以响应新的或更改的输入功能，数据样本，先验，类和环境。该研究计划旨在研究自适应多分类剂系统（AMCSS），这些系统可以在现实世界中的生物识别应用中实现高度的性能，并有效地更新生物识别模型，以响应从操作环境中新兴信息。这些AMCS会进化每个个体的二进制分类器（EOC）的集合，其中分类器是使用基于人群的进化优化共一训练的。在个人到系统的注册期间，一种新的动态多目标基于PSO的训练策略通过批处理数据样本来生成多样化的基本分类器库。然后，为了响应该个人的新数据，该策略要么生成一个与以前学习的分类器组合的额外池，要么通过增量学习来发展以前学习的分类器的池。然后根据精确度和多样性的专业度量从个人的池中选择分类器的子集。新的增量布尔组合技术被用来随着时间的流逝而适应决策级别的融合功能，以响应新的或不断变化的池。为了说明有限的数据和偏斜的分布，在ROC或其他空间中应用了增量BC。尽管本提案中描述的强大自适应技术可以应用于广泛的应用，但面部识别，签名验证和生物识别融合是这项研究的重点。为了加速该程序的所有步骤，新的AMCSS将通过高速GPGPU平台上的实际生物识别数据进行验证。