Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Face Recognition by Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition by elastic bunch graph matching
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow compensation in 2D images for face recognition
Pattern Recognition
Journal of Cognitive Neuroscience
A New Method of Illumination Invariant Face Recognition
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
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In this paper, we introduce an illumination normalization approach within frequency domain by utilizing Discrete Wavelet Transform (DWT) as a transformation function in order to suppress illumination variations and simultaneously amplify facial feature such as eyeball, eyebrow, nose, and mouth. The basic ideas are: 1) transform a face image from spatial domain into frequency domain and then obtain two major components, approximate coefficient (Low frequency) and detail coefficient (High frequency) separately 2) remove total variation in an image by adopting Total Variation Quotient Image (TVQI) or Logarithmic Total Variation (LTV) 3) amplify facial features, which are the significant key for face classification, by adopting Gaussian derivatives and Morphological operators respectively. The efficiency of our proposed approach is evaluated based on a public face database, Yale Face Database B, and its extend version, Extend Yale Face Database B. Our experimental results are demonstrated that the proposed approach archives high recognition rate even though only single image per person was used as the training set.