Facial recognition using principal component analysis based dimensionality reduction

This paper presents a comparison between one-dimensional component analysis (1D-PCA) and two-dimensional principal component analysis (2D-PCA) under two different types of classification techniques namely k-nearest neighbor (kNN) and Support Vector Machines (SVM). These two techniques differ in the method to determine the image covariance matrix. 2DPCA used 2D image matrices instead of column vectors in 1DPCA. The eigenvectors derived from these matrices will result in images in reduced dimensions to be used for classification. K-nearest neighbor algorithm (kNN) and Support Vector Machines (SVM) were used for classification. The performance measures used for comparison were classification accuracy and computational time. The tests were performed on the ORL image database. The experimental results indicate that 2DPCA outperforms in terms of classification accuracy and computational complexity.