A feature extractor and neural classifier for image recognition system are proposed. They are based on the permutative coding technique which continues our investigations on neural networks. It permits us to obtain sufficiently general description of the image to be recognized. Different types of images were used to test the proposed image recognition system. It was tested on the handwritten digit recognition problem, the face recognition problem and the shape of microobjects recognition problem. The results of testing are very promising. The error rate for the MNIST database is 0.44% and for the ORL database is 0.1%.

Meeting Name

IEEE International Joint Conference on Neural Networks, 2005


Electrical and Computer Engineering

Keywords and Phrases

MNIST Database; ORL Database; Face Recognition; Feature Extraction; Feature Extractor; Handwriting Recognition; Handwritten Digit Recognition; Image Coding; Image Recognition; Image Recognition System; Microobjects Recognition; Neural Classifier; Neural Nets; Neural Network; Object Recognition; Permutative Coding; Visual Databases

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2005