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Alternative TitleResearch on Feature Extraction of Frontal Face Image
Thesis Advisor刘迎建
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计算机应用技术
Keyword人脸识别 眼睛定位 Lbp特征 子空间降维 Dlda算法 Face Recognition Eye Localization Algorithm Lbp Feature Subspace-based Dimensionality Reducing Dlda Algorithm
Abstract本文关注的是正面人脸识别的特征提取,重点是研究人脸特征点定位(主要是眼睛定位)算法和人脸特征提取算法。 在特征点定位过程中,眼睛的位置是一个很重要的信息,它不仅对其他的特征点定位有指示和限定作用,而且是人脸核心区域归一化的一个非常重要的依据。在总结前人的工作的基础上,本文针对灰度人脸图像上的眼睛定位问题,提出了一个基于多层结构的快速眼睛定位算法。该算法首先利用极小值区域(MER)进行眼睛粗定位;然后使用三层筛选算法逐步去除错误的候选保留唯一的正确候选;最后使用眼睛精确定位算法来校正眼睛的位置。本算法在多个公开数据集上均有良好表现,是一个行之有效的方法。 在特征提取过程中,本文针对目前应用比较广泛的LBP特征进行了深入的研究。在对LBP特征和ULBP特征进行深入分析的基础上,本文提出了若干种改进特征。相对于经典的ULBP特征,本文提出的双一维LBP特征使用更少的维数得到了更好的结果。此外,针对通常的人脸核心区域的单一分块方法,我们也提出了一个多层分块方法,可以同时兼顾全局、局部和细节信息,并且降低了特征内部的线性相关性。 最后,在算法研究的基础上,本文实现了一个人脸识别系统,在实际应用中验证了我们提出的改进方法的有效性。
Other AbstractThe focus of this paper is the feature extraction from frontal face image. The two main parts are the algorithm of feature points locating (eye locating in particular) and the algorithm of feature extraction. The eye locations are very important landmarks in feature points locating process. They not only indicate and restrict the rough location of other feature points, but also act as significant evidence of normalization of ROI (Region of Interest). Based on the formal researchers’ work, a fast hierarchical-based eye-locating algorithm is proposed here to locate eyes in intensity face image. This new algorithm involves three main steps. First, MER (Minimum Extremal Region) is introduced here to locate eyes roughly. Then, a three-layer-filter is applied to rule out false detections step by step. Finally, an accurate eye-locating method is used to adjust the eye locations precisely. This algorithm has good results in many public face databases. Therefore, it is considered to be an effective method. In the step of feature extraction, this paper pays much attention to the widely-used LBP (Local Binary Pattern) feature. After carefully analysis to LBP and ULBP (Uniform LBP) feature, we propose several improvements on them. In contrary to standard ULBP feature, the Dual-1D LBP feature we proposed brings better results with less feature length. Besides, comparing to single grid on ROI of face image, we use multi grids instead, which can retrieve global, local and trivial information. It can also deduce the linear relativity within feature. Particularly, based on the algorithm research, we implement a face recognition system, which verifies the efficiency of our improvements or proposed methods.
Other Identifier200428014628019
Document Type学位论文
Recommended Citation
GB/T 7714
车昊. 正面人脸识别的特征提取研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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