二维多样性保持投影及人脸识别

doi:10.3969/j.issn.1001-2400.2012.06.006

Abstract

Abstract:

Previous work has demonstrated that manifold learning can effectively preserve the local geometry among nearby data, and has become an active topic in pattern recognition and machine learning. However, it ignores or even impairs the local diversity of data, which will impair the recognition accuracy and lead to unstable local geometrical structure representation. In this paper, a novel approach, namely two-dimensional diversity preserving projection (2DDPP), is proposed for dimensionality reduction. 2DDPP constructs an adjacency graph to model the variation of data and measures the variation among nearby data by the diversity scatter, on the basis of which a concise criterion is raised by maximizing the diversity scatter. Moreover, 2DDPP directly calculates the diversity scatter matrix from the image matrix, which effectively avoids the small sample size problem. Experiments on Yale, UMIST, and AR databases show the effecitveness of the proposed method.

Key words: diversity adjacency graph, manifold learning, feature extraction, face recognition

HOU Jun;HAO Xiujuan;XIE Deyan;GAO Quanxue. Face recognition using two-dimensional diversity preserving projection[J].J4, 2012, 39(6): 34-41.

References

［1］ Jiang X D. Linear Subspace-based Dimensionality Reduction ［J］. IEEE Signal Proc Magazine, 2011, 28(2): 16-26.
［2］ Yan S C, Xu D, Zhang B Y, et al. Graph Embedding and Extensions: a General Framework for Dimensionality Reduction ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
［3］ Fan Z Z, Xu Y, Zhang D. Local Linear Discriminant Analysis Framework Using Sample Neighbors［J］. IEEE Trans on Neural Networks, 2011, 22(7): 1119-1132.
［4］ Turk M, Pentland A P. Face Recognition Using Eigenfaces ［C］//Proc of IEEE Conference on Computer Vision and Pattern Recognition. Maui: IEEE Comput Sco Press, 1991: 586-591.
［5］ Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a New Approach to Appearance-based Face Representation and Recognition ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.
［6］高全学, 梁彦, 潘泉, 等. 基于描述特征的人脸识别研究［J］. 自动化学报, 2006, 32(3): 386-392.
Gao Quanxue, Liang Yan, Pan Quan, et al. Face Recognition Based on Expressive Features ［J］. ACTA Automatica Sinica, 2006, 32(3): 386-392.
［7］ Jiang X D. Asymmetric Principal Component and Discriminant Analyses for Pattern Classification ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(5): 931-937.
［8］ Luo D J, Ding C, Nie F P, et al. Cauchy Graph Embedding［C］//Proc of the 28th International Conference on Machine Learning. Bellevue: Association for Computing Machinery, 2011: 553-560.
［9］ Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction ［J］. Science, 2000, 290: 2319-2323.
［10］ Ham J, Lee D D, Mika S, et al. A Kernel View of the Dimensionality Reduction of Manifolds ［C］//Proc of Twenty-First International Conference on Machine Learning. Banff: Association for Computing Machinery, 2004: 369-376.
［11］ Scholkopf B, Smola A, Müller K R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem ［J］. Neural Computation, 1998, 15(5): 1299-1319.
［12］ He X F, Yan S C, Hu Y X, et al. Face Recognition Using Laplacianfaces ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
［13］ Xu Y, Song F X, Feng G, et al. A Novel Preserving Projection for Use with Face Recognition ［J］. Expert Systems with applications, 2010, 37: 6718-6721.
［14］ Gao Q X, Zhang H J, Liu J J. Two-dimensional Margin, Similarity and Variation Embedding ［J］. Neurocomputing, 2012, 86: 179-183.
［15］ Dai G, Yeung D Y. Tensor Embedding Methods ［C］//Proc of American Association for Artificial Intelligence (AAAI). Boston: American Association for Artificial Intelligence, 2006: 330-335.
［16］ Hu D W, Feng G Y, Zhou Z T. Two-dimensional Locality Preserving Projections (2DLPP) with Its Application to Palmprint Recognition ［J］. Pattern Recognition, 2007, 40 (1): 339-342.
［17］ Gao Q X, Xu H, Li Y Y, et al. Two-dimensional Supervised Local Similarity and Diversity Projection ［J］. Pattern Recognition, 2010, 43(10): 3359-3363.
［18］高全学, 谢德燕, 徐辉, 等. 融合局部结构和差异信息的监督特征提取方法［J］. 自动化学报, 2010, 36(8): 1107-1114.
Gao Quanxue, Xie Deyan, Xu Hui, et al. Supervised Feature Extraction Based on Information Fusion of Local Structure and Diversity Information ［J］. ACTA Automatica Sinica, 2010, 36(8): 1107-1114.
［19］ Liu K, Cheng Y, Yang J Y. Algebraic Feature Extraction for Image Recognition Based on an Optimal Discriminant Criterion ［J］. Pattern Recognition, 1993, 26(6): 903-911.
［20］ Yang J, Zhang D, Yang J Y, et al. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics ［J］. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(4): 650-664.

[1]	LIU Jieyi,GONG Maoguo,ZHAN Tao,LI Hao,ZHANG Mingyang. Method for discrimination of false targets in multistation radar systems based on the deep neural network [J]. Journal of Xidian University, 2021, 48(2): 133-138.
[2]	WEI Ziyu,YANG Xi,WANG Nannan,YANG Dong,GAO Xinbo. Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification [J]. Journal of Xidian University, 2021, 48(2): 205-212.
[3]	GUO Zekun,TIAN Long,HAN Ning,WANG Penghui,LIU Hongwei,CHEN Bo. Radar HRRP based few-shot target recognition with CNN-SSD [J]. Journal of Xidian University, 2021, 48(2): 7-14.
[4]	HAN Zhuoxi,WANG Feng,CHEN Pei,LI Zhuolun. Fuzzy data association algorithm assisted by historical features [J]. Journal of Xidian University, 2021, 48(2): 92-98.
[5]	WANG Xiaohua,YAO Pengchao,MA Liping,WANG Wenjie,ZHANG Lei. Algorithm for extraction of features of robot speech control in the factory environment [J]. Journal of Xidian University, 2020, 47(2): 16-22.
[6]	OUYANG Ning,WANG Xian’ao,CAI Xiaodong,LIN Leping. Low resolution face recognition method based on wavelet and recursive neural networks [J]. Journal of Xidian University, 2019, 46(6): 95-101.
[7]	WANG Hongyan,ZHU Limin,ZHANG Panjie,LI Jinping. Method for the detection of the piston side defect based on external contour registration [J]. Journal of Xidian University, 2019, 46(5): 75-83.
[8]	GUO Rui,ZANG Bo,PENG Shuming,XING Mengdao. Extraction of features of the urban high-rise building from high resolution InSAR data [J]. Journal of Xidian University, 2019, 46(4): 137-143.
[9]	LIU Daohua,CUI Yushuang,ZHAO Yansong,SONG Yuting,WANG Jinghui. Method for retrieving the teaching image based on the improved convolutional neural network [J]. Journal of Xidian University, 2019, 46(3): 52-58.
[10]	YANG Jiangong,WANG Xili,LIU Shigang. Spectral-spatial classification of hyperspectral images using deep Boltzmann machines [J]. Journal of Xidian University, 2019, 46(3): 109-115.
[11]	WANG Xinyuan,XIAO Song,LI Lei,JIAO Lingling. Robust target tracking algorithm based on the ELM and discriminative correlation filter [J]. Journal of Xidian University, 2019, 46(1): 57-63.
[12]	LIANG Xiaoxi, CAI Xiaodong, WANG Meng, KU Haohua. Face recognition method for integrating the nested residual CNN and angular metric [J]. Journal of Xidian University, 2018, 45(6): 144-149.
[13]	WANG Xing;HUANG Xiaoyu;LIU Xuanpu;KONG Xianguang;NIU Meng. Multi-layer incremental feature extraction method for industrial big data [J]. Journal of Xidian University, 2018, 45(4): 106-111.
[14]	HE Wangpeng;SUN Wei;SU Bo;YANG Yunyi;GUO Baolong. Sparse feature extraction technique and its applications to machinery fault diagnosis [J]. Journal of Xidian University, 2018, 45(2): 154-159.
[15]	ZHANG Yuhe;GENG Guohua;WEI Xiaoran;SHI Chenchen;ZHANG Shunli. Feature extraction of point clouds using the DBSCAN clustering [J]. Journal of Xidian University, 2017, 44(2): 114-120.

Face recognition using two-dimensional diversity preserving projection

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0