基于视觉特征提取和核判决分析方法的人脸识别
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摘要
人脸识别技术是模式识别领域的重要研究课题，这是由于人脸表情丰富、随年龄增长而变化，人脸图像受成像的光照、角度和距离等影响，人脸的自动识别非常困难，特别是要求机器在学习之后要有足够的泛化识别能力；同时人脸识别还涉及到图像处理、模式识别、生理学等知识。人脸识别广泛应用于人机交互、监控系统、门禁系统和档案管理等方面。人脸是具有自然相似结构，却又有弹性的局部可变形的非刚体目标，因此人脸识别的研究还有对大量同类目标识别问题具有推动作用。
人脸识别是一种特定内容的模式识别，从狭义上讲，就是将特定人脸上的特征提取和检测出来并与已知类别的标准样本的特征进行模式识别的操作，因此主要涉及特征提取和分类判决分析两方面。本文研究的工作主要是利用基于视觉特征提取方法，获得有效的人脸图像识别特征；并结合性能或速度改进了的核判决分析方法进行人脸图像识别。研究的内容主要包括：综合论述了人类视觉系统中与人脸识别有关的神经生理学和心理学研究成果；基于视觉皮层上皮细胞感受野的Gabor模型，提出了将递推Gabor滤波用于人脸图像特征的提取，并结合基于QR分解的核判决分析方法进行人脸识别的方法；基于视觉皮层上皮细胞感受野的高斯微分模型，提出了具有尺度和方向选择性的Hermite变换用于人脸图像特征的提取，并结合以类间分布最大化、总的分布最小化为准则的核判决分析方法进行人脸识别的方法；基于传输到人脑的视觉皮层的信号是压缩后具有傅立叶变换性质的信息，提出了在压缩域提取人脸图像的离散余弦特征描述，并结合零空间核判决分析方法进行人脸识别的方法；基于人类视觉系统被当作一个多通道模型进行描述，并且Marr理论指出信息处理的是分
Face recognition has a variety of potential applications in public security, surveillance, law enforcement, commerce, identity authentication, human-computer interfaces and e-services. During the past years, face recognition has received significant attention as a booming technology. In field settings, face images are subject to a wide range of variations. These include pose or view angle, illumination, occlusion, facial expression, time delay between image acquisition, and individual differences. The scalability of face recognition systems to such factors is not well understood. Face recognition in a perception system from human beings seems instinctive, but it is really a tough and complex task for a machine-based system. Recent face recognition surveys reveal that lighting changes, indoor-outdoor changes, pose variations and elapsed time databases are the critical parameters that greatly influence the performance of a face recognition system. But the effect from those parameters is significantly database dependent. Also, face recognition technology is related to the knowledge of image processing, pattern recognition, psychophysics and neuroscience. The human face is conformable in structure, and is elastic and deformable locally. The study of face recognition will improve the target recognition of the same kind.
A general statement of the problem of machine recognition of faces can be formulated as follows: given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces. The aim of this study is to
人脸识别是一种特定内容的模式识别，从狭义上讲，就是将特定人脸上的特征提取和检测出来并与已知类别的标准样本的特征进行模式识别的操作，因此主要涉及特征提取和分类判决分析两方面。本文研究的工作主要是利用基于视觉特征提取方法，获得有效的人脸图像识别特征；并结合性能或速度改进了的核判决分析方法进行人脸图像识别。研究的内容主要包括：综合论述了人类视觉系统中与人脸识别有关的神经生理学和心理学研究成果；基于视觉皮层上皮细胞感受野的Gabor模型，提出了将递推Gabor滤波用于人脸图像特征的提取，并结合基于QR分解的核判决分析方法进行人脸识别的方法；基于视觉皮层上皮细胞感受野的高斯微分模型，提出了具有尺度和方向选择性的Hermite变换用于人脸图像特征的提取，并结合以类间分布最大化、总的分布最小化为准则的核判决分析方法进行人脸识别的方法；基于传输到人脑的视觉皮层的信号是压缩后具有傅立叶变换性质的信息，提出了在压缩域提取人脸图像的离散余弦特征描述，并结合零空间核判决分析方法进行人脸识别的方法；基于人类视觉系统被当作一个多通道模型进行描述，并且Marr理论指出信息处理的是分
Face recognition has a variety of potential applications in public security, surveillance, law enforcement, commerce, identity authentication, human-computer interfaces and e-services. During the past years, face recognition has received significant attention as a booming technology. In field settings, face images are subject to a wide range of variations. These include pose or view angle, illumination, occlusion, facial expression, time delay between image acquisition, and individual differences. The scalability of face recognition systems to such factors is not well understood. Face recognition in a perception system from human beings seems instinctive, but it is really a tough and complex task for a machine-based system. Recent face recognition surveys reveal that lighting changes, indoor-outdoor changes, pose variations and elapsed time databases are the critical parameters that greatly influence the performance of a face recognition system. But the effect from those parameters is significantly database dependent. Also, face recognition technology is related to the knowledge of image processing, pattern recognition, psychophysics and neuroscience. The human face is conformable in structure, and is elastic and deformable locally. The study of face recognition will improve the target recognition of the same kind.
A general statement of the problem of machine recognition of faces can be formulated as follows: given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces. The aim of this study is to
引文
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