Abstract
We describe a method for dynamic emotion recognition from facial expression sequences. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM), encapsulating facial landmarks shapes which describe a given facial expression. We incorporate the dynamic model by learning the latent representation, with the aim to respect the data’s dynamics (facial shapes should maintain their correspondence along time). Then, a Gaussian process classifier is implemented to evaluate the relevance of the latent space features in the emotion recognition task. The results show that the proposed method can efficiently model a dynamic facial emotion and recognize with high accuracy a facial emotion sequence.
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García, H.F., Álvarez, M.A., Orozco, Á. (2014). Gaussian Process Dynamical Models for Emotion Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_77
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DOI: https://doi.org/10.1007/978-3-319-14364-4_77
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