Gonzalo Carvajal, Waldo Valenzuela, Miguel Figueroa
We describe an analog-VLSI neural network for face recognition based on subspace methods. The system uses a dimensionality-reduction network whose coeﬃcients can be either programmed or learned on-chip to per- form PCA, or programmed to perform LDA. A second network with user- programmed coeﬃcients performs classiﬁcation with Manhattan distances. The system uses on-chip compensation techniques to reduce the eﬀects of device mismatch. Using the ORL database with 12x12-pixel images, our circuit achieves up to 85% classiﬁcation performance (98% of an equivalent software implementation).