This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI) system. The temporal dynamics of such sequence of descriptors are represented by means of a Hankel matrix. The paper presents different strategies to compute dynamics-based representation of a sequence of FID, and reports classification accuracy values of the proposed representations within different standard classification frameworks. The representations have been validated in two very challenging application domains: emotion recognition and pain detection. Experiments on two publicly available benchmarks and comparison with state-of-the-art approaches demonstrate that the dynamics-based FID representation attains competitive performance when off-the- shelf classification tools are adopted.

Lo Presti, L., La Cascia, M. (2015). Using Hankel matrices for dynamics-based facial emotion recognition and pain detection. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on (pp. 26-33). IEEE [10.1109/CVPRW.2015.7301351].

Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

LO PRESTI, Liliana
;
LA CASCIA, Marco
2015-01-01

Abstract

This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI) system. The temporal dynamics of such sequence of descriptors are represented by means of a Hankel matrix. The paper presents different strategies to compute dynamics-based representation of a sequence of FID, and reports classification accuracy values of the proposed representations within different standard classification frameworks. The representations have been validated in two very challenging application domains: emotion recognition and pain detection. Experiments on two publicly available benchmarks and comparison with state-of-the-art approaches demonstrate that the dynamics-based FID representation attains competitive performance when off-the- shelf classification tools are adopted.
2015
978-1-4673-6759-2
Lo Presti, L., La Cascia, M. (2015). Using Hankel matrices for dynamics-based facial emotion recognition and pain detection. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on (pp. 26-33). IEEE [10.1109/CVPRW.2015.7301351].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/153946
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