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.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Iulian B. Ciocoiu
• Keywords: ESANN 2004, ESANN 2007, Face Recognition, Feature Vectors, Recurrent Neural Classifiers