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.
CITATION STYLE
García, H. F., Álvarez, M. A., & Orozco, Á. (2014). Gaussian process dynamical models for emotion recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8888, pp. 799–808). Springer Verlag. https://doi.org/10.1007/978-3-319-14364-4_77
Mendeley helps you to discover research relevant for your work.