Human Face Recognition based on Improved PCA Algorithm

Abstract

This paper aims to effectively recognize human faces from images, which is an important problem in the multimedia information process. After analyzing the related research works, the framework of the face recognition system is illustrated as first, which contains the training process and the testing process. Particularly, the improved PCA algorithm is use in the feature extraction module. The main innovations of this paper lie in that, in the improved PCA, we utilize a radial basis function to construct a kernel matrix by computing the distance of two different vectors, which are calculated by the parameter of 2-norm exponential. Afterwards, human faces can be recognized by computing the distance of test image and the training images by the nearest neighbor classifier, of which the cosine distance is utilized. Finally, experiments are conducted to make performance evaluation. Compared with the existing face recognition methods, the proposed scheme is more effective in recognizing human faces with high efficiency

PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

“…Furthermore, in the same year, Yue [ 24 ] proposed to use a radial basis function to construct a kernel matrix by computing the distance of two different vectors calculated by the parameter of 2-norm exponential and then applying a cosine distance to calculate the matching distance leading to higher recognition rate over a traditional PCA. Similarly, Min et al [ 17 ] introduced a two-dimensional concept for PCA (2DPCA) for face feature extraction to maintain the recognition rate but with lower computational recognition time.…”

“…For example, in 2013, I. Melnykov and V. Melnykov [ 41 ] proposed the use of Mahalanobis distance for K-mean algorithm to improve the performance when covariance matrices are not properly initialized, but with the increase of computational complexity. Moreover, soft computing-based approaches, that is, neural networks, are also used to compute the matching distance; for instance, Yue [ 24 ] used nearest neighbor methods to compute the matching distance to improve the classification precision. Zhou et al [ 14 ] also proposed a combination of PCA and LDA for image reconstruction to increase recognition rate and then be classified with SVM.…”

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

“…PCA is theoretically the optimal linear scheme, in terms of least mean square error, for compressing a set of high dimensional vectors into a set of lower dimensional vectors and then reconstructing the original set (Sharma, Paliwal, Imoto, & Miyano, 2013;Deng & Tian, 2013;. It is a non-parametric analysis and the answer is unique and independent of any hypothesis about data probability distribution (Raajan et al, 2012;Meng & Zheng, 2013;Yue & Li, 2013;Zhu & Xu, 2013;Ran, Bao-Gang, Wei-Shi, & Xiang-Wei, 2011).…”