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低分辨率、异质人脸识别算法与嵌入式人脸识别系统研究
其他题名Study on Low-resolution and Heterogeneous Face Recognition and an Embedded-system Solution of Real Face Recognition System
周长涛
学位类型工学博士
导师李子青
2011-12-01
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词人脸识别 低分辨率 异质图像匹配 嵌入式系统 Face Recognition Low Resolution Heterogeneous Image Matching Embedded System
摘要人脸识别是模式识别与计算机视觉领域的热点研究内容,在个人身份认证与识别、安检、智能人机交互等领域有着广泛的应用前景。现有的人脸识别算法深受低分辨率、异质图像模态等因素的影响,限制了其在实际应用场合中性能的发挥。 此外,当前国内外日益严峻的社会安全形势也需要更加方便快捷的身份识别和验证技术,在生物特征识别领域形成中国完全自主知识产权的身份验证理论、方法和技术手段(产品)势在必行。 本文正是站在这样的时代背景下,从目前人脸识别研究所面临的主要困难以及现实应用中对新一代人脸识别系统的重大需求这两个方面,来展开的研究和讨论。具体来说,一方面本文研究了低分辨率人脸识别和异质人脸匹配两个问题,并给出了鲁棒、有效的算法;另一方面,本文与当前主流工业技术相结合,探讨并给出了面向国家公共安全需求的、配置灵活、稳定可靠的新一代人脸识别系统的设计方案,为自主知识产权的人脸识别产品的最终定型、生产做了有益的尝试。本文的主要内容及贡献归纳为如下四个方面: (1)针对低分比率人脸识别问题,本文将监督学习引入到投影子空间的学习当中来,提出了一种称为同时鉴别分析的方法。 通过将维度不同的高、低分辨率人脸图像映射到相同维度的公共空间中,它们本身所具有的鉴别性之间差异被最小化了; 同时,分别用于高、低分辨率映射的投影矩阵在同时进行的学习过程中,却保证了在共同的子空间中人脸图像基于身份 的鉴别性被最大化。 (2)针对异质人脸图像匹配问题,本文在对偶谱回归的框架下,提出了一种改进的谱回归方法。改进的对偶谱回归算法在新的观点之下, 将投影变换看成是所有模态下样本数据线性组合的结果,从而使得最终学习得到的变换能够提取更多的鉴别信息,提高了整个算法的 精确性;同时,改进的对偶谱回归算法又将局部性约束引入了算法框架,进一步提升了其推广性。 (3)基于双核架构处理器TM320DM6446,本文提出了一个面向实际应用的嵌入式 人脸识别系统硬件平台设计方案。该平台计算能力强大,硬件资源丰富,且能够根据应用需求灵活配置,方便与其它系统和设备进行协同和集成。 (4)在嵌入式Linux操作系统及Davinci~提供的Codec Engine~基础软件框架的基础之上,本文提出了一种适于人脸识别软件系统的MVC架构方案。该方案在保证了系统功能的同时,极大地降低了各个软件组件之间的耦合性,使得最终的应用软件系统非常易于协同开发和维护。 总的来说,本文比较深入的研究了低分辨率人脸识别、异质人脸图像匹配和面向实际应用的嵌入式人脸识别系统整体解决方案三问题,并提出了相应的改进方法和设计方案,为促进人脸识别算法的性能提高与走向实际应用做了有益的探索和尝试。
其他摘要Face recognition is an important problem in pattern recognition and computer vision field. Due to various adverse factors such as low-resolution and heterogeneous modality variations, the performance of most existing face recognition algorithms is far behind satisfactory and hence limits its application and performance in real world. In addition, the increasingly serious situation of public security in the world needs more convenient identification and authentication technologies which can help or boost the traditional ones. For China, it is imperative to develop our own solutions with full intellectual property rights. Under this background, this thesis focuses on the main difficulties in face recognition research and attempts to provide a practical system design. Specifically, it proposes two algorithms which deal with low-resolution face recognition and heterogeneous face matching , respectively. And it also give a whole solution of real face recognition based on the new generation of embedded-system technologies, which is powerful, robust and configurable to be integrated to or cooperate with other working systems. The main contributions of this thesis include following issues: (1)To deal with low-resolution face recognition, we introduce supervised methods to subspace leaning and proposed a novel idea, e.g. simultaneous discriminant analysis(SDA). SDA learns two mappings from LR and HR images respectively to a common subspace where discrimination property is maximized. In SDA, the data gap between LR and HR is reduced by mapping into a common space; and the mapping is designed for preserving most discriminative information. (2)Coupled spectral regression (CSR) is an effective frame-work for heterogeneous face recognition. But in original CSR, the coupled projections are derived from the corresponding modality data respectively. The mutual information between different modalities are not sufficiently explored. So in this thesis, we propose to make up the projections by both(all) modality data, by which the discriminative information hidden in all samples are sufficiently explored. Moreover, the sample locality information is introduced and integrated into the proposed algorithm to improve its generalization ability. (3)Based on the dual-core processer TM320DM6446, the thesis propose a novel hardware design for practical face recognition system. This platform has powerful computation ability and plenty of resources and at the mean time i...
馆藏号XWLW1710
其他标识符200818014628080
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6405
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
周长涛. 低分辨率、异质人脸识别算法与嵌入式人脸识别系统研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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