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Alternative Titlelocal feature based face recognition: probability model and new methods
Thesis Advisor李子青
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword生物特征识别 人脸识别 马尔可夫随机场 贝叶斯理论 异质图像 多模态融合征识别 Biometrics Face Recognition Markov Random Field Bayesian Principle Heterogeneous Images Multi-modal Fusion
Abstract由于公共安全、信息安全等领域的应用需求,生物特征识别技术引起了广泛关注。其中人脸识别技术因为其独特的优势,具备非常大的市场应用前景。在图像模式识别学术上,人脸识别技术研究也有其重要的意义。 本论文以提升人脸识别技术水平为目的,在统计图像分析和模式识别理论指导下,以图像人脸局部特征为基础,研究由局部特征到全局决策的人脸分类识别的概率模型和新方法。本文在以下三个方向进行了研究:一、基于局部特征和马尔可夫随机场理论的人脸图像建模和识别方法;二、基于局部特征的异质人脸图像生成和匹配方法;三、融合基于局部特征的多源和多模态信息的生物识别方法。 以下为本论文的主要工作及贡献: 1.在马尔可夫随机场和贝叶斯理论框架下,将局部图像特征间的关系作为人脸识别重要的约束条件,提出了一种基于马尔可夫随机场的人脸识别模型;通过实验证明了该方法的有效性,为人脸识别研究方引入了新的方法,并提供了一个用经典理论解决实际问题的示范。 2.借鉴图像纹理分析的理论和图像合成理论,提出了“人脸类推”的概念,通过局部归一化和图像映射,将近红外图像变换成相应的可见光图像,从而将异质图像匹配问题转化成传同质图像人脸识别问题;为异质图像人脸匹配问题提供了一个新的解决方法。 3.提出了一种新的多模态融合方法,对近红外人脸、可见光人脸、和虹膜等多模态,在分数层进行非线性融合;通过在实际数据库上对各种基于分数层的传统融合方法的比较实验,验证了该融合方法的有效性;为提升人脸识别核心技术,提供了一个多模态融合的解决方法。 最后,本论文设计并实现了一个能在实际应用中使用的近红外人脸和可见光人脸融合的识别系统,系统从硬件和软件两部分保证了其实用性,为人脸识别技术的市场推广做了一定的贡献。
Other AbstractBiometric technologies have attracted much attention from around the world due to demands in the public security and information security sectors. Face recognition has significant advantages over other biometric modalities, and hence has tremendous application prospects. Not only so, research on face recognition presents great academic significance in image pattern recognition. This thesis is aimed to develop better face recognition technologies. It conducts research towards novel probability models and new methods based on local features using statistical image analysis and pattern recognition theories and techniques. Global decision of face classification is performed from local feature based classifiers. The thesis contains new research in the following three directions: (1) Face modeling and recognition methods based on local feature and Markov random fields; (2) Heterogeneous face synthesis and recognition based on face analogy and local features; (3) multi-modal biometric fusion of local feature based biometric recognition results. The contributions of the thesis are the following: 1. It proposes a Markov Random Field (MRF) based face recognition model, in the framework of Markov Random Field and Bayesian decision theories. There, constraints on local features as well as contextual relationships between them are explored and encoded into a cost function. The proposed MRF method, validated by experiments, provides a new perspective for modeling the face recognition problem. 2. Inspired by image texture analysis and synthesis, it proposes a new conception of “face analogy”. By local normalization and face analogy, Near Infrared (NIR) face images can be converted into VIS (Visual) face images. Thereby, heterogeneous face recognition problem can be solved using homogeneous face matching methods. This provides a novel approach for heterogenous face recognition. 3. It introduces an effective multi-modal recognition algorithm by fusing NIR face, VIS face and iris images, in which nonlinear score level fusion rules are developed. Improved performance is obtained by experiments. Finally, a NIR-VIS multi-modal face recognition system, composed of hardware and software, is developed for practical applications.
Other Identifier200618014628050
Document Type学位论文
Recommended Citation
GB/T 7714
王睿. 基于局部特征的人脸识别研究:概率模型和新方法[D]. 中国科学院自动化研究所. 中国科学院研究生院,2010.
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