Volume measure in 2DPCA-based face recognition

Abstract

Two-dimensional principal component analysis (2DPCA) is based on the 2D images rather than 1D vectorized images like PCA, which is a classical feature extraction technique in face recognition. Many 2DPCA-based face recognition approaches pay a lot of attention to the feature extraction, but fail to pay necessary attention to the classification measures. The typical classification measure used in 2DPCA-based face recognition is the sum of the Euclidean distance between two feature vectors in a feature matrix, called distance measure (DM). However, this measure is not compatible with the high-dimensional geometry theory. So a new classification measure compatible with high-dimensional geometry theory and based on matrix volume is developed for 2DPCA-based face recognition. To assess the performance of 2DPCA with the volume measure (VM), experiments were performed on two famous face databases, i.e. Yale and FERET, and the experimental results indicate that the proposed 2DPCA + VM can outperform the typical 2DPCA + DM and PCA in face recognition.