The classification criterion for the two dimensional LDA (2DLDA)-based face recognition methods has been little
considered, while we almost pay all attention to the 2DLDA-based feature extraction. The typical classification measure
used in 2DLDA-based face recognition is the sum of the Euclidean distance between two feature vectors in feature
matrix, called traditional distance measure (TDM). However, this classification criterion does not match the high
dimensional geometry space theory. So we apply the volume measure (VM), which is based on the high dimensional
geometry theory, to the 2DLDA-based face recognition in this paper. To test its performance, experiments were
performed on the YALE face databases. The experimental results show the volume measure (VM) is more efficient than
the TDM in 2DLDA-based face recognition.