Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Separating Style and Content with Bilinear Models
Neural Computation
Illumination-robust face recognition using ridge regressive bilinear models
Pattern Recognition Letters
Expression-invariant face recognition by facial expression transformations
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper proposes an efficient face recognition system where images are acquired under different camera positions and lighting conditions. Active Appearance model is used to obtain shape and appearance information from faces in the form of feature vectors. Bilinear model then works upon these vectors to obtain style specific basis matrices in the training phase. In the test phase the bilinear model uses elastic net regularization to determine stable content vectors using style specific basis matrix. Euclidean distance between content vectors of two images is used to take decision on matching. The proposed system has been tested on 1255 images of 108 subjects. Experiment results reveal that the system achieves an accuracy of 95% when five top best matches are considered in a closed set identification setup.