Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line-Based Face Recognition under Varying Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
Eigenpaxels and a neural-network approach to image classification
IEEE Transactions on Neural Networks
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This paper introduces a new face recognition method that treats 2D face images as 1D signals to take full advantages of wavelet multi-resolution analysis. Though there have been many applications of wavelet multi-resolution analysis to recognition tasks, the effectiveness of the approach on 2D images of varying lighting conditions, poses, and facial expressions remains to be resolved. We present a new face recognition method and the results of extensive experiments of the new method on the ORL face database, using a neural network classifier trained by randomly selected faces. We demonstrate that the method is computationally efficient and robust in dealing with variations in face images. The performance of the method also decreases gracefully with the reduction of the number of training faces.