Title :
Accelerating backpropagation in human face recognition
Author :
Evans, D.J. ; Ahmad Fadzil, M.H. ; Zainuddin, Zahir
Author_Institution :
Dept. of Comput. Studies, Loughborough Univ. of Technol., UK
Abstract :
Standard backpropagation, as with many gradient based optimization methods converges slowly as neural network training problems become larger and more complex. This paper describes the employment of two algorithms to accelerate the training procedure in an automatic human face recognition system. As compared to standard backpropagation, the convergence rate is improved by up to 98% with only a minimal increase in the complexity of each iteration
Keywords :
backpropagation; convergence; face recognition; image classification; multilayer perceptrons; optimisation; visual databases; backpropagation; convergence rate; gradient based optimization methods; human face recognition; neural network training problems; Acceleration; Appropriate technology; Convergence; Employment; Face recognition; Humans; Neural networks; Neurons; Optimization methods; Pixel;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.613974
