Title :
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
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;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
DOI :
10.1109/ICMLA.2012.196