scholarly journals Efficient Kernel-based Two-Dimensional Principal Component Analysis for Smile Stages Recognition

2012 ◽  
Vol 10 (1) ◽  
pp. 113 ◽  
Author(s):  
Rima Tri Wahyuningrum ◽  
Fitri Damayanti
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional

Optimal mean two-dimensional principal component analysis with F-norm minimization

2017 ◽  
Vol 68 ◽  
pp. 286-294 ◽  
Author(s):  
Qianqian Wang ◽  
Quanxue Gao ◽  
Xinbo Gao ◽  
Feiping Nie
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Norm Minimization

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
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.

scholarly journals Early warning of tsunami from seismo-ionospheric fluctuation after Japan’s March 11, 2011, M=9.0 Tohoku earthquake using two-dimensional principal component analysis

2015 ◽  
Vol 58 (4) ◽  
Author(s):  
Jyh-Woei Lin
Keyword(s):  
Principal Component Analysis ◽  
Shock Waves ◽  
Early Warning ◽  
Principal Component ◽  
Tohoku Earthquake ◽  
Component Analysis ◽  
Satellite System ◽  
Electron Content ◽  
Two Dimensional ◽  
Total Electron

<p>Two-dimensional principal component analysis (2DPCA) is implemented to analyze the total electron content (TEC) anomalies after Japan’s Tohoku earthquake that occurred at 05:46 on March 11, 2011 (UTC) (M<sub>w</sub>=9). 2DPCA and TEC data processing were conducted just after this devastating earthquake. Analysis results show an earthquake-associated TEC anomaly near the epicenter that began at 05:47. This may represent an extension of the precursor of the earthquake, to the precursor of China’s Wenchuan earthquake on May 12, 2008, detected by the study of Lin [2012], for which the data were obtained at a height of 150-200 km by the FORMOSAT-3 satellite system. It is impossible that such precursor caused by the acoustic shock waves. Another TEC anomaly near the epicenter occurred at 05:53, and this initiated the propagation of the tsunami effect related to the ionosphere through the acoustic shock waves from the epicenter. However, the TEC anomalies did not appear to be affected by a contemporaneous geomagnetic storm and other non-earthquake effects. The propagation of anomalous fluctuation could be an early warning of the tsunami for the regions far from the epicenter as it began to propagate with the higher speed of 3960-4950 km/h than the tsunami speed (720-800 km/h).</p><div> </div>

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