New self-organizing maps with non-conventional metrics and their applications for iris recognition and automatic translation

Authors:
Victor-Emil Neagoe
Affiliations:
Department Electronics, Telecommunications and Information Technology, Polytechnic University of Bucharest, Bucharest, Romania
Venue:
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Year:
2007

Citing 2
Cited 1

Syntactic and structural pattern recognition

Syntactic and structural pattern recognition
Self-Organizing Maps

Self-Organizing Maps

Multimodal Algorithm for Iris Recognition with Local Topological Descriptors

CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper has as aim the design and applications of two self-organizing maps using nonconventional metrics. First approach concerns the Levensthein Self-Organizing Map (LSOM). The LSOM is a SOM that uses a symbolic representation for both the input and also for the weight rows and it is based on the Levensthein metrics. The software implementation of the experimental LSOM model is designed for automatic Romanian-English translation of 1009 words and expressions. The second approach corresponds to the Hamming Self-Organizing Map (HSOM). The HSOM is a SOM that uses binary representation for input and weight vectors and is based on Hamming metric. We have implemented the HSOM for recognition of iris binary templates, and we have evaluated its performances for CASIA iris database with 108 subjects. A recognition score of 99.08% is obtained.