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Article Dans Une Revue Expert Systems with Applications Année : 2020

Fusing transformed deep and shallow features (FTDS) for image-based facial expression recognition

Résumé

In this paper, we propose combining between the transformed hand-crafted and deep features using PCA to recognize the six-basic facial expressions from static images. To evaluate our approach, we use three popular databases (CK+, CASIA and MMI). We introduce the use of the Pyramid Multi Level (PML) face representation for facial expression recognition. The hand-crafted features are obtained with such representations. Initially, we determine the optimal level of the PML features of three hand-crafted descriptors (HOG, LPQ and BSIF) using CK+, CASIA and MMI databases. After the optimal level of the PML is found for each descriptor, we combine them together with the transformed final VGG-face layers (FC6 and FC7) in order to get a compact image descriptor. In within-database experiments, our approach achieved higher accuracy than the state-of-art methods on both the CK+ and CASIA databases, and competitive result on the MMI database. Likewise, our approach outperformed the static methods in all six experiments of cross-databases. (C) 2020 Published by Elsevier Ltd.
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Dates et versions

hal-03321560 , version 1 (17-08-2021)

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Fares Bougourzi, Fadi Dornaika, K. Mokrani, Abdelmalik Taleb-Ahmed, Yassine Ruichek. Fusing transformed deep and shallow features (FTDS) for image-based facial expression recognition. Expert Systems with Applications, 2020, 156, pp.113459. ⟨10.1016/j.eswa.2020.113459⟩. ⟨hal-03321560⟩
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