Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Flexible images: matching and recognition using learned deformations
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending the Feature Vector for Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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To efficiently solve human face image recognition problem with an image database, many techniques have been proposed. A key step in these techniques is the extraction of features for indexing in the database and afterwards for fulfilling recognition tasks. Linear Discriminate Analysis(LDA) is a statistic method for classification. LDA filter is global in space and local in frequency. It squeezes all discriminant information into few basis vectors so that the interpretation of the extracted features becomes difficult. In this paper, we propose a new idea to enhance the performance of the LDA method for image recognition. We extract localized information of the human face images by virtue of wavelet transform. The simulation results suggest good classification ability of our proposed system.