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基于多个再生核希尔伯特空间的多角度人脸识别
Multi-angle Face Recognition Algorithm Based on Multi-RKHS

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林硕 1   龚志恒 1 *   韩忠华 1   史海波 2  
文摘 针对传统谱算法在人脸识别中的局限,提出一种基于多个再生核希尔伯特空间的多角度人脸识别算法.首先,利用landmark标记法对图像进行预处理,得到训练图像的角度;其次,通过人脸数据的多次核化迭代,使其在构建的再生核希尔伯特空间中呈线性,针对不同类型的人脸数据,建立多个再生核希尔伯特空间;最后,通过对比训练图像,判断待检测人脸图像的再生核希尔伯特空间归属,实现多角度人脸识别.选取FERET和CMU-PIE两类数据集进行对比实验,实验结果表明:所提出的算法不仅在平均识别率上高于传统算法5%,平均识别效率也较传统算法提高20%.
其他语种文摘 The traditional spectrum algorithms are limited in face recognition problem. For its characteristics of problem, a novel method based on multi-reproducing Kernel Hilbert space was proposed. Firstly, the images were processed by the landmark method, and the angle of training images could be obtained. Secondly, the face data was iterated by the Kernel, then face data expressed linearly in the reproducing Kernel Hilbert space. Thereafter, for many types of face data, the multi-reproducing Kernel Hilbert space were established. Finally, the reproducing Kernel Hilbert space belonging of human face image was judged by the comparison of training images, and the multi-angle face recognition achieved. The two classes of data sets were selected as the experimental data, which consisted of FERET and CMU-PIE. A large number of experiments were carried out. The results show that the proposed method has great effect to recognise multi-angle face. The average recognition rate and efficiency are 5% and 20% higher than the traditional algorithms, respectively.
来源 光子学报 ,2013,42(12):1436-1441 【核心库】
DOI 10.3788/gzxb20134212.1436
关键词 谱算法 ; 多角度 ; 预处理 ; 核希尔伯特空间 ; 迭代
地址

1. 沈阳建筑大学信息与控制工程学院, 沈阳, 110168  

2. 中国科学院沈阳自动化研究所, 沈阳, 110016

语种 中文
文献类型 研究性论文
ISSN 1004-4213
学科 自动化技术、计算机技术
基金 国家自然科学基金 ;  国家重大科技专项
文献收藏号 CSCD:5016011

参考文献 共 20 共1页

1.  刘中华. 一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法. 光子学报,2011,40(4):636-641 被引 8    
2.  杨红芳. 基于波段调制的HOSVD多光谱人脸识别. 光子学报,2010,39(4):750-754 被引 2    
3.  沈壁川. 人脸检测中盲解卷积的点传输函数和光学传输函数分析. 光子学报,2010,39(9):1652-1657 被引 1    
4.  Mittag F. Use of support vector machines for disease risk prediction in genome-wide association studies: Concerns and opportunities. Human Mutation,2012,33(12):1708-1718 被引 4    
5.  Cruz-Mota J. Scale invariant feature transform on the sphere: Theory and applications. International Journal of Computer Vision,2012,98(2):217-241 被引 13    
6.  Hua Chunsheng. Pedestrian detection by using a spatio-temporal histogram of oriented gradients. IEICE Transactions on Information and Systems,2013,96(6):1376-1386 被引 1    
7.  Marchetti A. The VIMOS Public Extragalactic Redshift Survey (VIPERS): spectral classification through principal component analysis. Monthly Notices of the Royal Astronomical Society,2012,428(2):1424-1437 被引 1    
8.  Roweis S T. Nonlinear dimensionality reduction by locally linear embedding. Science,2000,290(5500):2323-2326 被引 1298    
9.  Cevikalp H. Face recognition based on image sets. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2010:2567-2573 被引 5    
10.  Liu Xiuwen. Optimal linear representa-tions of images for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5):662-666 被引 2    
11.  Wright J. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227 被引 925    
12.  Hamm J. Grassmann discriminant analysis: a unifying view on subspace-based learning. Proceedings of the 25th international conference on Machine learning,2008:376-383 被引 9    
13.  Wang Tiesheng. Kernel Grassmannian distances and discriminant analysis for face recognition from image sets. Pattern Recognition Letters,2009,30(13):1161-1165 被引 2    
14.  Harandi M T. Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2011:2705-2712 被引 7    
15.  Bennett K P. MARK: A boosting algorithm for heterogeneo us Kernel models. Proceedings of the eighthACM SIGKDD international conference on Knowledge discovery and data mining,2002:24-31 被引 1    
16.  Lanckriet G. Learning the Kernel matrix with semi-definite programming. Journal Machine Learning Research,2004,5:27-72 被引 35    
17.  Xiong H L. Optimizing the Kernel in the empirical feature space. IEEE Transactions on Neural Networks,2005,16(2):460-474 被引 24    
18.  Chen B. Optimizing the data-dependent Kernel under a unified Kernel optimization framework. Pattern Recognition,2007,41(6):2107-2119 被引 2    
19.  王峰. 最优双核复合分类算法的构造. 电子学报,2012,40(2):260-265 被引 6    
20.  Zhu X X. Face detection, pose estimation, and landmark localization in the wild. Proceedings of IEEE Conference on Com-Puter Vision and Pattern Recognition,2012:2879-2886 被引 1    
引证文献 1

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