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基于统计学习与融合的人脸识别关键问题研究
其他题名Study on Key Issues of Automatic Face Recognition Based on Statistic Learning and Fusion
高勇
学位类型工学博士
导师王阳生
2007-06-20
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
关键词自动人脸识别 统计学习 融合 Gabor小波 Boosting 线性判别分析 随机子空间 Automatic Face Recognition Statistical Learning Fusion Gabor Wavelet Boosting Linear Discriminant Analysis Random Subspace
摘要自动人脸识别的研究有着重要的学术价值和广泛的应用前景。本文沿着统计学习与融合的思路对人脸识别中的对齐、特征表达和分类器的训练及构造三个关键问题进行了深入的研究。在研究的过程中,充分的考虑到人脸模式的特殊性和统计人脸识别中的小样本问题,在此基础上将融合的方法引入到统计学习的框架中,达到提高人脸识别性能的目的。论文的主要贡献如下: 1)从融合的思想出发,提出了一种多分类器融合的人眼定位算法。利用统计学习算法Boosting训练得到四个具有不同属性的人眼检测器。最后通过Dempster-Shafer证据理论对四个人眼检测器的输出结果进行融合,确定真实眼睛的位置。实验证明,该方法具有较好的定位精度。 2)为了充分利用Gabor提取的判别信息,提出了一种加权的Gabor复数特征(WGCF)用于人脸识别。本文将Gabor幅度特征和相位特征用加权的办法组合成复数特征,即WGCF,达到同时利用两种判别信息的目的。同时,将欧式空间的子空间识别算法推广至复数空间,使WGCF特征很方便的和子空间算法相结合。论文中的实验 表明WGCF要好于单独的Gabor幅度和相位特征。另外,论文在这一部分还探讨了子空间识别算法中距离度量的问题, 得出了一些较为实用的结论。 3)从人脸模式的特殊性和特征表达的鲁棒性出发,提出了一种基于分离区域对线性判别分析的表达方法用于人脸识别。 论文采用多个分离的区域对作为表达人脸的基本单元,并进一步从每个区域对中提取线性判别特征。用所有区域对线性判别特征的相似度加权之和来表示两张人脸的相似度。该算法中的参数--区域对的位置和加权系数,采用数据驱动的方法自动获得。实验证明分离区域对线性判别特征具有较强的表达和判别能力,基于这一表达的识别 算法能够取得好的识别精度。 4)为提高Boosting算法的效率和缓解人脸识别中的小样本问题,提出了基于随机子空间的Boosting分类器训练方法。 该方法从原始的特征空间中产生多个随机子空间,在每个特征子空间独立的进行Boosting训练和识别,最后将多个强分类器加以组合。这样的处理使得Boosting训练效率得到了大幅度的提高。由于每个分类器训练的特征空间维数有所降低,小样本问题也同时得到了缓解。而最后的组合,则由于各分类器相异性进一步提高了系统的识别精度。 大量的实验证明了该方法的合理性和有效性。
其他摘要The research on Automatic Face Recognition (AFR) has both significant academic importance and wide applications. Based on statistical learning and fusion, the three key problems in AFR--- alignment, feature representation and training and construction of classifier, are studied in this thesis deeply. With consideration on particularity of human face image and small sample size problem in statistical face recognition, this dissertation incorporates the technology of fusion into statistical learning so as to improve performance of AFR. And the main contribution of this thesis includes: Firstly, a multiple-classifier based eye location method is proposed from the viewpoint of fusion. Totally four eye detectors with different properties are trained by statistical learning algorithm Boosting. Then Dempster-shafer theory is used to combine outputs of the four eye detectors and decide true eye location. Experimental results demonstrate this eye location method is precise. Secondly, a Weighted Gabor Complex Feature (WGCF) is proposed to fully make use of discriminant information extracted by Gabor wavelet. The Gabor magnitude and phase feature vector are combined into one complex vector by proper weights so as to use both kinds of information. Meanwhile, the subspace based recognition algorithms, Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) in Euclidean space, are generalized into Unitary space, which makes WGCF easily be used with them. Experimental results show that WGCF is better than Gabor magnitude and phase feature. Thirdly, with consideration on speciality of human face pattern and robustness of feature representation, a face recognition algorithm based on Dissociated Region Pair Linear Discriminant Analysis (DRP-LDA) feature is proposed. Multiple dissociated region pairs (DRP) are used to represent a face image and then the Linear Discriminant Analysis (LDA) feature is extracted from each DRP. Similarity of two faces is the weighted sum of similarities of LDA features of multiple DRPs that are used to represent a face.The parameters including DRP positions and weight coefficients are learned by data driven method in the framework of statistical learning. Experimental results show the algorithm is effective and the DRP representation is powerful. Fourthly, a Boosting in random subspaces face recognition method is proposed. Multiple random subspaces are generated from original feature space and training is conducted in each feature subspace independently. Then strong classifiers trained from multiple random subspaces are combined into one stronger classifier for face recognition. This method improves efficiency of Boosting, especially training process, greatly. Extensive experimental results show that the method is reasonable and effective.
馆藏号XWLW1136
其他标识符200418014628074
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6034
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
高勇. 基于统计学习与融合的人脸识别关键问题研究[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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