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  • 1.
    Mikheeva, Olga
    KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
    Perceptual facial expression representation2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Facial expressions play an important role in such areas as human communication or medical state evaluation. For machine learning tasks in those areas, it would be beneficial to have a representation of facial expressions which corresponds to human similarity perception.

    In this work, the data-driven approach to representation learning of facial expressions is taken. The methodology is built upon Variational Autoencoders and eliminates the appearance-related features from the latent space by using neutral facial expressions as additional inputs. In order to improve the quality of the learned representation, we modify the prior distribution of the latent variable to impose the structure on the latent space that is consistent with human perception of facial expressions.

    We conduct the experiments on two datasets and the additionally collected similarity data, show that the human-like topology in the latent representation helps to improve the performance on the stereotypical emotion classification task and demonstrate the benefits of using a probabilistic generative model in exploring the roles of latent dimensions through the generative process.

  • 2.
    Mikheeva, Olga
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Ek, C. H.
    Kjellström, Hedvig
    KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
    Perceptual facial expression representation2018In: Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 179-186Conference paper (Refereed)
    Abstract [en]

    Dissimilarity measures are often used as a proxy or a handle to reason about data. This can be problematic, as the data representation is often a consequence of the capturing process or how the data is visualized, rather than a reflection of the semantics that we want to extract. Facial expressions are a subtle and essential part of human communication but they are challenging to extract from current representations. In this paper we present a method that is capable of learning semantic representations of faces in a data driven manner. Our approach uses sparse human supervision which our method grounds in the data. We provide experimental justification of our approach showing that our representation improves the performance for emotion classification.

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