Hyperspectral face recognition with a spatial information fusion for local dynamic texture patterns and collaborative representation classifier

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Tai-Xiang Jiang ◽

Ting-Zhu Huang ◽

Xi-Le Zhao ◽

Tian-Hui Ma

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Nearest Neighbor ◽

Spatial Information ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Face Images ◽

Pca Method ◽

Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

Preserving spatial information and overcoming variations in appearance for face recognition

Pattern Analysis and Applications ◽

10.1007/s10044-010-0188-4 ◽

2010 ◽

Vol 14 (1) ◽

pp. 67-75 ◽

Cited By ~ 3

Author(s):

Önsen Toygar ◽

Hakan Altınçay

Keyword(s):

Face Recognition ◽

Spatial Information

Joint collaborative representation algorithm for face recognition

The Journal of Supercomputing ◽

10.1007/s11227-018-2606-0 ◽

2018 ◽

Vol 75 (5) ◽

pp. 2304-2314

Author(s):

Xincan Fan ◽

Kaiyang Liu ◽

Haibo Yi

Keyword(s):

Face Recognition ◽

Collaborative Representation

Collaborative Representation of Statistically Independent Filters’ Response: An Application to Face Recognition Under Illicit Drug Abuse Alterations

Image Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-319-59129-2_38 ◽

2017 ◽

pp. 448-458

Author(s):

Raghavendra Ramachandra ◽

Kiran Raja ◽

Sushma Venkatesh ◽

Christoph Busch

Keyword(s):

Face Recognition ◽

Drug Abuse ◽

Illicit Drug ◽

Collaborative Representation ◽

Illicit Drug Abuse

Patch Based Face Recognition via Fast Collaborative Representation Based Classification and Expression Insensitive Two-Stage Voting

Computational Science and Its Applications – ICCSA 2016 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-42108-7_44 ◽

2016 ◽

pp. 562-570 ◽

Cited By ~ 2

Author(s):

Decheng Yang ◽

Weiting Chen ◽

Jiangtao Wang ◽

Yan Xu

Keyword(s):

Face Recognition ◽

Collaborative Representation ◽

Two Stage

Discriminative Weighted Low-Rank Collaborative Representation Classifier for Robust Face Recognition

Biometric Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-319-97909-0_28 ◽

2018 ◽

pp. 257-264

Author(s):

Xielian Hou ◽

Caikou Chen ◽

Shengwei Zhou ◽

Jingshan Li

Keyword(s):

Face Recognition ◽

Low Rank ◽

Collaborative Representation ◽

Robust Face Recognition

Manifold Smoothed Class-specific Discriminant Collaborative Representation for Face Recognition

2019 12th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2019.10124 ◽

2019 ◽

Author(s):

Lou Songjiang ◽

Ma Yanghui ◽

Zhou Wujie

Keyword(s):

Face Recognition ◽

Collaborative Representation

An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges

Information Fusion ◽

10.1016/j.inffus.2020.01.007 ◽

2020 ◽

Vol 59 ◽

pp. 59-83 ◽

Cited By ~ 18

Author(s):

Maryam Imani ◽

Hassan Ghassemian

Keyword(s):

Image Classification ◽

Information Fusion ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Current Trends

Locality Preserving Collaborative Representation for Face Recognition

Neural Processing Letters ◽

10.1007/s11063-016-9558-2 ◽

2016 ◽

Vol 45 (3) ◽

pp. 967-979 ◽

Cited By ~ 8

Author(s):

Taisong Jin ◽

Zhiling Liu ◽

Zhengtao Yu ◽

Xiaoping Min ◽

Lingling Li

Keyword(s):

Face Recognition ◽

Collaborative Representation ◽

Locality Preserving