A fully-differential CMOS implementation of Oja's learning rule in a dual-synapse neuron for extracting principal components for face recognition

A fully-differential CMOS implementation of Oja's learning rule in a dual-synapse neuron for extracting principal components for face recognition

A fully-differential, CMOS implementation of a self-organizing, dual-synapse neuron with on-chip learning for real-time facial feature extraction is presented. The adaptation of the network follows Oja's learning rule and the synaptic weight vector is shown to adapt to the principal component v...

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Bibliographic Details
Published in:42nd Midwest Symposium on Circuits and Systems (Cat. No.99CH36356) Vol. 2; pp. 1102 - 1104 vol. 2
Main Authors: Spencer, R.G., Sanchez-Sinencio, E.
Format: Conference Proceeding
Language:English
Published: IEEE 1999
Subjects:
Autocorrelation
Data mining
Face recognition
Facial features
Flow graphs
Network synthesis
Neurons
Principal component analysis
Signal synthesis
Vectors
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Summary:A fully-differential, CMOS implementation of a self-organizing, dual-synapse neuron with on-chip learning for real-time facial feature extraction is presented. The adaptation of the network follows Oja's learning rule and the synaptic weight vector is shown to adapt to the principal component vector of the set of two-dimensional input vectors.
ISBN:0780354915
9780780354913
DOI:10.1109/MWSCAS.1999.867829