Abstract
This paper presents the use of WiSARD and ClusWiSARD weightless neural networks models for the classification of the contraction and extension of Action Units, the facial muscles involved in emotive expressions. This is a complex problem due to the large number of very similar classes, and because it is a multi-label classification task, where the positive expression of one class can modify the response of the others. WiSARD and ClusWiSARD solutions are proposed and validated using the CK+ dataset, producing responses with accuracy of 89.66%. Some of the major works in the field are cited here, but a proper comparison is not possible due to a lack of appropriate information about such solutions, such as the subset of classes used and the time of training/testing. The contribution of this paper is in the pioneering use of weightless neural networks in an AUs classification task, in the unpublished application of the WiSARD and ClusWiSARD models in multi-label tasks and in the new unsupervised expansion of ClusWiSARD proposed here.
This work was partially supported by CAPES, CNPq, FAPERJ and FINEP, Brazilian research agencies.
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References
Abbasnejad, I., et al.: Using synthetic data to improve facial expression analysis with 3d convolutional networks. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) (2017)
Aleksander, I., Thomas, W., Bowden, P.: Wisard, a radical new step forward in image recognition. Sensor Rev. 4(3), 120–124 (1984)
Almaev, T., Martinez, B., Valstar, M.: Learning to transfer: transferring latent task structures and its application to person-specific facial action unit detection. In: IEEE International Conference on Computer Vision (ICCV), pp. 3774–3782 (2015)
Bettadapura, V.: Face expression recognition and analysis: the state of the art. arXiv:1203.6722
Breuer, R., Kimmel, R.: A deep learning perspective on the origin of facial expressions (2017)
Cardoso, D.O., et al.: Financial credit analysis via a clustering weightless neural classifier. Neurocomputing 183, 70–78 (2016)
Cardoso, D., De Gregorio, M., Lima, P., Gama, J., França, F.: A weightless neural network-based approach for stream data clustering. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 328–335. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32639-4_40
Cardoso, D.O., Lima, P.M.V., de Gregorio, M., et al.: Clustering data streams with weightless neural networks. In: ESANN 2011, 19th European Symposium on Artificial Neural Networks, Bruges, Belgium (2011)
Chu, W.S., la Torre, F.D., Cohn, J.F.: Learning spatial and temporal cues for multi-label facial action unit detection (2016)
Ekman, P., W.V.F.: Manual for the facial action coding system (1977)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of IEEE International Conference Face and Gesture Recognition (AFGR 2000), pp. 46–53 (2000)
Martinez, B., Valstar, M.F., Jiang, B., Pantic, M.: Automatic analysis of facial actions: a survey. IEEE Trans. Affect. Comput. (2018). https://doi.org/10.1109/TAFFC.2017.2731763
Pons, G., Masip, D.: Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition (2018)
Sauvola, J., Pietikainen, M.: Adaptive document image binarization. Pattern Recognit. 33, 225–236 (2000)
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Filho, L.A.D.L., Guarisa, G.P., Oliveira, L.F.R., Filho, A.L., França, F.M.G., Lima, P.M.V. (2019). Action Units Classification Using ClusWiSARD. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_33
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