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非约束环境下人脸识别系统的研究与实现
Alternative TitleResearch and Implementation of Face Recognition System in Unconstrained Environments
周吉
Subtype工程硕士
Thesis Advisor肖柏华
2013-05-28
Degree Grantor中国科学院大学
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机技术
Keyword人脸识别 全局特征 Fourier变换 局部特征 局部相位量化 排序测度 全局和局部特征融合 Face Recognition Global Feature Fourier Transform Local Feature Local Phase Quantization Ordinal Measures Fusing Of Global And Local Features
Abstract作为计算机视觉、模式识别、图像处理、机器学习等许多学科的一个交叉研究热点,人脸识别近年来受到了越来越多研究者的重视。经过几十年来的发展,人脸识别技术取得了长足的进步。大量的性能评测结果表明,在理想的约束环境下,最好的人脸识别算法已经可以取得很高的性能。但是在非约束环境下,光照、姿态、表情等因素引起的变化导致人脸图像的类内变化有时会大于类间变化,导致人脸识别系统无法取得令人满意的识别率。要开发出一个鲁棒实用的人脸识别系统,还需要解决大量的关键问题,尤其需要研究高效的人脸特征表示和快速精确的核心识别算法。 特征表示作为人脸识别算法的关键所在,受到了极大的关注。本文从全局、局部和融合三个方面综述了人脸识别中常用的特征表示方法。局部特征对光照、姿态、表情等产生的变化具有较好的鲁棒性,因而在人脸识别中受到了广泛的关注。本文提出了基于局部相位量化和排序测度特征相融合的局部特征表示方法。相关文献指出全局和局部特征对于人脸识别结果都是有影响的,本文基于此观点提出了并行集成全局和局部特征的方法,取得了不错的实验效果。本文的主要工作包括: (1)提出了一种称为局部相位量化排序测度直方图(HOLPQOM)的局部特征。该局部特征融合了局部相位量化(LPQ)和排序测度(OM)特征,继承了LPQ特征对图像模糊的鲁棒性和OM特征的光照不变性。该特征的提取过程是首先针对原始图像提取LPQ特征,在此基础上进一步提取OM特征。由于OM特征值是二进制编码,为了获得更加密实的特征表示,本文作者将极子数相同、极子间距离相同、极子方向不同的4种LPQOM特征值合并成一个范围在 的十进制数,最后分块提取直方图特征。在LFW数据库的实验结果表明该局部特征相比其他一些常用的局部特征,具有更好的鲁棒性。 (2)提出了将全局Fourier特征和局部HOLPQOM特征并行集成的人脸识别框架。在该框架中,基于全局特征的分类器和基于局部特征的分类器以加权方式融合成整体分类器。在LFW数据库的对比实验说明,局部特征的分类能力要优于全局特征的能力。进一步的实验又表明这两种特征具有一定的互补性,将它们融合之后可以显著提升人脸识别算法的准确率。 (3)设计并实现了一个基于人脸识别技术的智能视频监控系统。本文将融合全局和局部特征的方法作为系统的核心算法,以LFW数据库作为训练数据,以实验室人员的身份证照片和现场拍摄视频作为测试数据,进行了实时的人脸检测和识别实验,验证了算法的实用性。
Other AbstractAs a cross-disciplinary topic of many research areas (e.g. computer vision, pattern recognition, image processing, machine learning, at.al.), face recognition has attracted more and more researchers’ attention in recent years. In the past few decades, much progress has been made in this area. Many performance evaluation results show that the best face recognition algorithm has achieved high performance under the ideal constrained environment. However, under the unconstrained environment, the variations caused by the change in factors such as illumination, pose, expression and so on, could be larger than those caused by identity change. This makes face recognition systems unable to obtain a desirable recognition rate. In order to develop a robust and practical face recognition system, many key problems need to be solved, such as effective feature representation and robust recognition algorithm. Feature representation is generally regarded as fundamental of face recognition algorithm and has received much attention. This thesis reviews the widely used feature representation methods in face recognition from global, local and fused aspects. Local feature is robust to the variations due to lighting, pose, expression or other factors. Thus it attracts much attention in face recognition. This dissertation proposes a novel local feature representation by fusing local phase quantization and ordinal measures features. Related literatures state that global and local features are both essential to face recognition. Basing on this conclusion, this thesis proposes to combine global and local features by a parallel manner, and obtains the boosted face performance. On the whole, the contributions of this dissertation are summarized as follows: (1) Propose a novel local feature representation named Histograms of Local Phase Quantization Ordinal Measures (HOLPQOM). The LPQ is robust to image blurring and the OM is robust to the illumination. We fuse these two features in order to inherit the advantages of them and get a novel representation. The process of extracting this local feature is as follows: we extract the LPQ feature of the original image and then extract the OM feature of the obtained LPQ feature. The value of OM feature is binary. In order to obtain a compact representation, we encode the values of each LPQOM for four orientations at a given lobe number and a given inter-lobe distance into a single decimal number ranging from 0 to 15. Finally, we com...
shelfnumXWLW1931
Other Identifier2010E8014669008
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7669
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
周吉. 非约束环境下人脸识别系统的研究与实现[D]. 中国科学院自动化研究所. 中国科学院大学,2013.
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