Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian face recognition using deformable intensity surfaces
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Journal of Cognitive Neuroscience
A fast multiresolution feature matching algorithm for exhaustive search in large image databases
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper a novel technique for face recognition is proposed. Using the statistical Local Feature Analysis (LFA) technique, a set of feature points is extracted from each face image, at locations with highest deviations from the statistical expected face. Each feature point is described by a set of Gabor wavelet responses at different frequencies and orientations. A triangle-inequality-based pruning algorithm is developed for fast matching, which automatically chooses a set of key features from the database of model features and uses the pre-computed distances of the keys to the database, along with the triangle inequality, in order to speedily compute lower bounds on the distances from a query feature to the database, and eliminate the unnecessary direct comparisons. Our proposed technique achieves perfect results on the ORL face set and an accuracy rate of 99.1% on the FERET face set, which shows the superiority of the proposed technique over all considered state-of-the-art face recognition methods.