Subspace-Based Face Recognition in Analog VLSI

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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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).