Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Performance evaluation of face recognition algorithms on Asian face database
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A neural scheme for robust detection of transparent logos in TV programs
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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In this paper, we present a new statistical projection-based face recognition method, called Bilinear Discriminant Analysis (BDA). The proposed technique effectively combines two complementary versions of Two-Dimensional-Oriented Linear Discriminant Analysis (2DoLDA), namely Column-Oriented Linear Discriminant Analysis (CoLDA) and Row-Oriented Linear Discriminant Analysis (RoLDA). BDA relies on the maximization of a generalized bilinear projection-based Fisher criterion. A series of experiments was performed on various international face image databases in order to evaluate and compare the effectiveness of BDA to RoLDA and CoLDA. The experimental results indicate that BDA outperforms RoLDA, CoLDA and 2DPCA for face recognition, while leading to a significant dimensionality reduction.