Fully automatic 3D facial expression recognition using local depth features

M. Xue, Ajmal Mian, W. Liu, L. Li

    Research output: Chapter in Book/Conference paperConference paper

    11 Citations (Scopus)

    Abstract

    Facial expressions form a significant part of our nonverbal communications and understanding them is essential for effective human computer interaction. Due to the diversity of facial geometry and expressions, automatic expression recognition is a challenging task. This paper deals with the problem of person-independent facial expression recognition from a single 3D scan. We consider only the 3D shape because facial expressions are mostly encoded in facial geometry deformations rather than textures. Unlike the majority of existing works, our method is fully automatic including the detection of landmarks. We detect the four eye corners and nose tip in real time on the depth image and its gradients using Haar-like features and AdaBoost classifier. From these five points, another 25 heuristic points are defined to extract local depth features for representing facial expressions. The depth features are projected to a lower dimensional linear subspace where feature selection is performed by maximizing their relevance and minimizing their redundancy. The selected features are then used to train a multi-class SVM for the final classification. Experiments on the benchmark BU-3DFE database show that the proposed method outperforms existing automatic techniques, and is comparable even to the approaches using manual landmarks. © 2014 IEEE.
    Original languageEnglish
    Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision (WACV)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1096-1103
    ISBN (Print)9781479949854
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE Winter Conference on Applications of Computer Vision - Steamboat Springs, United States
    Duration: 24 Mar 201426 Mar 2014

    Conference

    Conference2014 IEEE Winter Conference on Applications of Computer Vision
    CountryUnited States
    CitySteamboat Springs
    Period24/03/1426/03/14

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  • Cite this

    Xue, M., Mian, A., Liu, W., & Li, L. (2014). Fully automatic 3D facial expression recognition using local depth features. In 2014 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1096-1103). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2014.6835736