Enhancement and extensions of principal component analysis for face recognition

Abstract

Primarily due to increasing security demands and potential commercial and law enforcement applications, automatic face recognition has been a subject of extensive study in the past several decades, and remains an active field of research as of today. As a result, numerous techniques and algorithms for face recognition have been developed, many of them proving effective in one way or another. Nevertheless, it has been realized that constructing good solutions for automatic face recognition remains to be a challenge. The last two decades have witnessed significant progress in the development of new methods for automatic face recognition, some being effective and robust against pose, illumination and facial expression variations, while others being able to deal with large-scale data sets. On all accounts, the development of state-of-the-art face recognition systems has been recognized as one of the most successful applications of image analysis and understanding. Among others, the principal component analysis (PCA) developed in the early 1990s has been a popular unsupervised statistical method for data analysis, compression and visualization, and its application to face recognition problems has proven particularly successful. The importance of PCA consists in providing an efficient data compression with reduced information loss, and efficient implementation using singular value decomposition (SVD) of the data matrix. Since its original proposal, many variations of the standard PCA algorithm have emerged. This thesis is about enhancement and extensions of the standard PCA for face recognition. Our contributions are twofold. First, we develop a set of effective pre-processing techniques that can be employed prior to PCA in order to obtain improved recognition rate. Among these, a technique known as perfect histogram matching (PHM) is shown to perform very well. Other pre-processing methods we present in this thesis include an extended sparse PCA algorithm for dimensionality reduction, a wavelet-transform and total variation minimization technique for dealing with noisy test images, and an occlusion-resolving algorithm. Second, we propose an extended two-dimensional PCA method for face recognition. This method, especially when combined with a PHM pre-processing module, is found to provide superior performance in terms of both recognition rate and computational complexity.