Subspace-Based Face Recognition in Analog VLSI
Abstract
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).
Cite
Text
Carvajal et al. "Subspace-Based Face Recognition in Analog VLSI." Neural Information Processing Systems, 2007.Markdown
[Carvajal et al. "Subspace-Based Face Recognition in Analog VLSI." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/carvajal2007neurips-subspacebased/)BibTeX
@inproceedings{carvajal2007neurips-subspacebased,
title = {{Subspace-Based Face Recognition in Analog VLSI}},
author = {Carvajal, Gonzalo and Valenzuela, Waldo and Figueroa, Miguel},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {225-232},
url = {https://mlanthology.org/neurips/2007/carvajal2007neurips-subspacebased/}
}