Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
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
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Handbook of Face Recognition
An efficient illumination normalization method for face recognition
Pattern Recognition Letters
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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While much progress has been made in face recognition over the last decades, changes in illumination directions still remain as a difficult problem. In this paper, we propose an efficient image normalization method which can overcome illumination effects effectively. The proposed method is based on intensity distribution transformation. However, instead of applying it globally, transformation in intensity distribution is performed for each column independently using one frontal mean face as a reference image. Since it does not require image processing such as image segmentation, the computational complexity is very low and it can circumvent boundary discontinuity caused by region segmentation. Extensive experimental results using Feret database and extended Yale B database demonstrate the competence of the proposed method.