Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
The kernel orthogonal mutual subspace method and its application to 3D object recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A framework for 3d object recognition using the kernel constrained mutual subspace method
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Extended fisher criterion based on auto-correlation matrix information
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
| Hi-index | 0.00 |
I hope to start from one question. "Is the eigenface[1] a subspace method?" Answer is weakly YES and strongly NO. In wide meaning in Subspace method of pattern recognition is that uses subspace. In this meaning the answer is YES. However in narrow meaning the term "Subspace method" means pattern recognition techniques that represent class featuring information with subspace of original feature space[2]. The eigenface subspace represent common feature of trained faces, that is differ from class information. Thus in this meaning the answer is NO. For understanding the term of "Subspace method", we shall trace back to a Subspace method root. In this article I try to clarify the meaning of Subspace method through the historical study. To this goal we trace histories of Subspace methods from their birth at 1960s to 21c. We studied the history both side of theory and applications, because sometimes new theory is inspired by new application and new theory extend applicability of Subspace methods.