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ESANN
2004

Face Recognition Using Recurrent High-Order Associative Memories

9 years 19 days ago
Face Recognition Using Recurrent High-Order Associative Memories
A novel face recognition approach is proposed, based on the use of compressed discriminative features and recurrent neural classifiers. Low-dimensional feature vectors are extracted through a combined effect of wavelet decomposition and subspace projections. The classifier is implemented as a special gradient-type recurrent analog neural network acting as an associative memory. The system exhibits stable equilibrium points in predefined positions given by the feature vectors of the training set. Experimental results for the Olivetti database are reported, indicating improved performances over standard PCA and LDA-based face recognition approaches.
Iulian B. Ciocoiu
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where ESANN
Authors Iulian B. Ciocoiu
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