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.
This work is partially supported by TMWFK-Grant # B509-03007 to H.-M. Gross.
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Wilhelm, T., Böhme, HJ., Gross, HM. (2005). Classification of Face Images for Gender, Age, Facial Expression, and Identity. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_89
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DOI: https://doi.org/10.1007/11550822_89
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