Advertisement

A Citation k-NN Approach for Facial Expression Recognition

  • Daniel Acevedo
  • Pablo Negri
  • María Elena Buemi
  • Francisco Gómez Fernández
  • Marta Mejail
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we propose a descriptor based on areas and angles of triangles formed by the landmarks from face images. We test this descriptors for facial expression recognition by means of an adaptation of the k-Nearest Neighbors classifier called Citation-kNN in which the training examples come in the form of sets of feature vectors. Comparisons with other state-of-the-art techniques on the CK+ dataset are shown. The descriptor remains robust and precise in the recognition of expressions.

References

  1. 1.
    Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. PAMI 37, 1113–1133 (2015)CrossRefGoogle Scholar
  2. 2.
    Zen, G., Porzi, L., Sangineto, E., Ricci, E., Sebe, N.: Learning personalized models for facial expression analysis and gesture recognition. IEEE Trans. Multimed. 18, 775–788 (2016)CrossRefGoogle Scholar
  3. 3.
    Lucey, P., Cohn, J., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The CK+ Dataset: a complete dataset for action unit and emotion-specified expression. In: CVPRW, pp. 94–101 (2010)Google Scholar
  4. 4.
    Acevedo, D., Negri, P., Buemi, M.E., Mejail, M.: Facial expression recognition based on static and dynamic approaches. In: ICPR, pp. 4124–4129 (2016)Google Scholar
  5. 5.
    Bottino, A., Vieira, T., Ul Islam, I.: Geometric and textural cues for automatic kinship verification. Int. J. Pattern Recogn. 29 (2015)Google Scholar
  6. 6.
    Osman Ali, A., et al.: Age-invariant face recognition using triangle geometric features. Int. J. Pattern Recogn. Artif. Intell. 29, 1556006 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Corneanu, C.A., et al.: Survey on RGB, 3D, thermal, and multimodal approaches for facial expression recognition: history, trends, and affect-related applications. IEEE Trans. Pattern Anal. 38, 1548–1568 (2016)CrossRefGoogle Scholar
  8. 8.
    Ubalde, S., Gómez-Fernández, F., Goussies, N.A., Mejail, M.: Skeleton-based action recognition using citation-kNN on bags of time-stamped pose descriptors. In: ICIP, pp. 3051–3055 (2016)Google Scholar
  9. 9.
    Wang, J., Zucker, J.D.: Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th International Conference on Machine Learning ICML 2000, pp. 1119–1126 (2000)Google Scholar
  10. 10.
    Sanin, A., Sanderson, C., Harandi, M., Lovell, B.: Spatio-temporal covariance descriptors for action and gesture recognition. In: WACV, pp. 103–110 (2013)Google Scholar
  11. 11.
    Wang, Z., Wang, S., Ji, Q.: Capturing complex spatio-temporal relations among facial muscles for facial expression recognition. In: CVPR, pp. 3422–3429 (2013)Google Scholar
  12. 12.
    Chew, S.W., Lucey, P., Lucey, S., Saragih, J., Cohn, J., Sridharan, S.: Person-independent facial expression detection using constrained local models. In: FG 2011, pp. 915–920 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Facultad de Ciencias Exactas y Naturales, Departamento de ComputaciónUniversidad de Buenos AiresBuenos AiresArgentina
  2. 2.Instituto de Investigación en Ciencias de la Computación (ICC)CONICET-Universidad de Buenos AiresBuenos AiresArgentina
  3. 3.CONICET-Universidad Argentina de la Empresa (UADE)Buenos AiresArgentina

Personalised recommendations