Classification of face images for gender, age, facial expression, and identity

Authors:
Torsten Wilhelm;Hans-Joachim Böhme;Horst-Michael Gross
Affiliations:
Department of Neuroinformatics and Cognitive Robotics, Ilmenau Technical University, Ilmenau, Germany;Department of Neuroinformatics and Cognitive Robotics, Ilmenau Technical University, Ilmenau, Germany;Department of Neuroinformatics and Cognitive Robotics, Ilmenau Technical University, Ilmenau, Germany
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 5
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Abstract

In this paper we compare two models for extracting features from face images and several neural classifiers for their applicability to classify gender, age, facial expression, and identity. These models are i) a description of face images by their projection on independent base images and ii) an Active Appearance Model which describes the shape and grey value variations of the face images. The extracted feature vectors are classified with Nearest Neighbor, MLP, RBF and LVQ networks, and classification results are compared.