Open Science Research Excellence
@article{(International Science Index):,
  title    = {Analysis of Feature Space for a 2d/3d Vision based Emotion Recognition Method},
  author    = {Robert Niese and  Ayoub Al-Hamadi and  Bernd Michaelis},
  country   = {},
  abstract  = {In modern human computer interaction systems
(HCI), emotion recognition is becoming an imperative characteristic.
The quest for effective and reliable emotion recognition in HCI has
resulted in a need for better face detection, feature extraction and
classification. In this paper we present results of feature space analysis
after briefly explaining our fully automatic vision based emotion
recognition method. We demonstrate the compactness of the feature
space and show how the 2d/3d based method achieves superior features
for the purpose of emotion classification. Also it is exposed that
through feature normalization a widely person independent feature
space is created. As a consequence, the classifier architecture has
only a minor influence on the classification result. This is particularly
elucidated with the help of confusion matrices. For this purpose
advanced classification algorithms, such as Support Vector Machines
and Artificial Neural Networks are employed, as well as the simple k-
Nearest Neighbor classifier.},
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {3},
  number    = {11},
  year      = {2009},
  pages     = {2156 - 2161},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 35, 2009},