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
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
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In this paper, we present a new approach for face recognition, named Modular Bilinear Discriminant Analysis (MBDA). In a first step, a set of experts is created, each one being trained independently on specific face regions using a new supervised technique named Bilinear Discriminant Analysis (BDA). BDA relies on the maximization of a generalized Fisher criterion based on bilinear projections of face image matrices. In a second step, the experts are combined to assign an identity with a confidence measure to each of the query faces. A series of experiments is performed in order to evaluate and compare the effectiveness of MBDA with respect to BDA and to the Modular Eigenspaces method. The experimental results indicate that MBDA is more effective than both BDA and the Modular Eigenspaces approach for face recognition.