VLSI implementation of a skin detector based on a neural network

Authors

Farid Boussaid, University of Western Australia
Abdesselam Bouzerdoum, University of WollongongFollow
Douglas Chai, Edith Cowan University

RIS ID

11933

Publication Details

Boussaid, F., Bouzerdoum, A. & Chai, D. (2005). VLSI implementation of a skin detector based on a neural network. In O. Pinngern, W. Bejapopolakui & Y. Tan (Eds.), International Conference on Information and Communications Security (ICICS 2005) (pp. 1561-1564). USA: IEEE.

Abstract

This paper describes the VLSI implementation of a skin detector based on a neural network. The proposed skin detector uses a multilayer perception with three inputs, one hidden layer, one output neuron and a saturating linear activation function to simplify the hardware implementation. The skin detector achieves a classification accuracy of 88.76%. To reduce mismatch associated errors, a single skin detection processing unit is used to classify all pixels of the input RGB image. The current-mode fully analog skin detection processing circuitry only performs computations during the read-out phase, enabling real-time processing. Fully programmable, the proposed skin detection processing circuitry allows for the external control of all classifier parameters to compensate for mismatch and changing lighting conditions.