Two dimensional LDA using volume measure in face recognition

Jicheng Meng; Li Feng; Xiaolong Zheng

doi:10.1117/12.751564

15 November 2007 Two dimensional LDA using volume measure in face recognition

Jicheng Meng, Li Feng, Xiaolong Zheng

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67882G (2007) https://doi.org/10.1117/12.751564
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

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.

Citation Download Citation

Jicheng Meng, Li Feng, and Xiaolong Zheng "Two dimensional LDA using volume measure in face recognition", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882G (15 November 2007); https://doi.org/10.1117/12.751564

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