Patch-Based Principal Component Analysis for Face Recognition

Abstract

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.

Facial Pain Expression Recognition in Real-Time Videos

“…Jiang et al defined patches as fundamental units that could be used to identify an object, including a face, and included the combination of a patch-based model and principal component analysis. They opined that patches were more useful in the recognition and identification of an object than pixels [ 20 ]. The authors discussed the use of automated pain detection using machines that had been trained using facial action units.…”

Optimal principal component analysis of STEM XEDS spectrum images

“…It can be shown that among all possible linear projections, PCA ensures the smallest Euclidean difference between the initial and projected datasets or, in other words, provides the minimal least squares errors when approximating data with a smaller number of variables [10]. Due to that, PCA has found a lot of applications in imaging science for data compression, denoising and pattern recognition (see for example [11][12][13][14][15][16][17][18]) including applications to STEM XEDS spectrum-imaging [19][20][21][22][23][24].…”

Segmentation of dust storm areas on Mars images using principal component analysis and neural network

“…Turk and Pentland (1991) introduced eigenfaces using principal component analysis (PCA) for face recognition. Jiang et al (2017) also used the patch-based method and PCA for face recognition. In this study, we apply the combination of the patch-based approach and PCA to Mars images to segment Martian dust storms.…”