ICMLA Face Recognition Challenge -- Results of the Team Computational Intelligence Mittweida

Author

Villmann, Thomas ; Kastner, Margit ; Nebel, D. ; Riedel, Morris

Author_Institution

Comput. Intell. Group, Univ. of Appl. Sci. Mittweida, Mittweida, Germany

Volume

2

fYear

2012

fDate

12-15 Dec. 2012

Firstpage

592

Lastpage

595

Abstract

The contribution describes the application of the Team ´Computational Intelligence Group´ from the University of Applied Sciences Mittweida (Germany) to the ICMLA Face Recognition Challenge 2012. In particular we explain the data preprocessing and feature extraction, which was applied before classification learning. Further we give details about the used classification algorithm - the enhanced generalized matrix learning vector quantization model (eGMLVQ). We provide information about the results as well as observed classification properties detected by the learning algorithm.

Keywords

face recognition; feature extraction; image classification; learning (artificial intelligence); matrix algebra; vector quantisation; ICMLA; classification algorithm; classification learning algorithm; eGMLVQ; enhanced generalized matrix learning vector quantization; face recognition; feature extraction; team computational intelligence group; Avatars; Correlation; Educational institutions; Humans; Prototypes; Vector quantization; Vectors; classification; face recognition; learning vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications (ICMLA), 2012 11th International Conference on

Conference_Location

Boca Raton, FL

Print_ISBN

978-1-4673-4651-1

Type

conf

DOI

10.1109/ICMLA.2012.196

Filename

6406802