Monte Carlo Based Importance Estimation of Localized Feature Descriptors for the Recognition of Facial Expressions

The automated and exact identification of facial expressions in human computer interaction scenarios is a challenging but necessary task to recognize human emotions by a machine learning system. The human face consists of regions whose elements contribute to single expressions in a different manner. This work aims to shed light onto the importance of specific facial regions to provide information which can be used to discriminate between different facial expressions from a statistical pattern recognition perspective. A sampling based classification approach is used to reveal informative locations in the face. The results are expression-sensitive importance maps that indicate regions of high discriminative power which can be used for various applications.

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