Principal Component Analysis Integrating Mahalanobis Distance for Face Recognition

Abstract

In machine learning and pattern recognition, principal component analysis (PCA) is a very popular feature extraction and dimensionality reduction method for improving recognition performance or computational effiency. It has been widely used in numerous applications, especially in face recognition. Researches often use PCA integrating the nearest neighbor classifier (NNC) based on Euclidean distance (ED) to classify face images. We refer to this method as PCA+ED. However, we have observed that PCA can not significantly improve the recognition performance of NNC based on Euclidean distance through many experiments. The main reason is that PCA can not significantly change the Euclidean distance between samples when many components are used in classification. In order to improve the classification performance in face recognition, we use another distance measure, i.e., Mahalanobis distance (MD), in NNC after performing PCA in this paper. This approach is referred to as PCA+MD. Several experiments show that PCA+MD can significantly improve the classification performance in face recognition.