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Facial Expression Recognition Using Robust Local Directional Strength Pattern Features and Recurrent Neural Network

Rokkones, Anders Skibeli; Uddin, Md Zia; Tørresen, Jim
Book chapter; AcceptedVersion; Peer reviewed
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IEEECE-Berlin_Paper.pdf (1.251Mb)
Year
2019
Permanent link
http://urn.nb.no/URN:NBN:no-76794

CRIStin
1799246

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  • Institutt for informatikk [3586]
  • CRIStin høstingsarkiv [15167]
Original version
Proceedings of IEEE International Conference on Consumer Electronics-Berlin. 2019, 2019-September, 283-288, DOI: https://doi.org/10.1109/ICCE-Berlin47944.2019.8966234
Abstract
This work proposes a novel facial expression recognition approach to contribute to better human-machine interactions. To do that, edge features in facial expression images are combined with a recurrent neural network (RNN) to classify different facial expressions. Robust edge features are first obtained by using Local Directional Strength Pattern (LDSP) and applied with RNN. This LDSP-RNN approach achieves superior recognition performance than other conventional approaches on a randomly distributed training and testing datasets obtained from a public dataset. The proposed approach should be useful for various practical applications such as a robot analyzing and understanding different human emotions from facial expressions based on robotic vision.
 
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