Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Scattered Data Interpolation with Multilevel B-Splines
IEEE Transactions on Visualization and Computer Graphics
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
On finding differences between faces
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
3D face recognition using stereoscopic vision
ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics
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This paper presents a new face recognition system, based on Multilevel B-splines and Support Vector Machines. The idea is to consider face images as heightfields, in which the height relative to each pixel is given by the corresponding gray level. Such heightfields are approximated using Multilevel B-Splines, and the coefficients of approximation are used as features for the classification process, which is performed using Support Vector Machines. The proposed approach was thoroughly tested, using ORL, Yale, Stirling and Bern face databases. The obtained results are very encouraging, outperforming traditional methods like eigenface, elastic matching or neural-networks based recognition systems.