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A dynamic fusion of features from deep learning and the HOG-TOP algorithm for facial expression recognition

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Abstract

Facial expression recognition plays an essential role in surveillance videos, anxiety treatment, expression analysis, gesture recognition, computer games, patient monitoring, operator fatigue detection, and robotics. Therefore, facial expression recognition has attracted more and more attention over the years and became a difficult task because emotion can be influenced by several factors. Some approaches based on Deep Convolutional Neural Networks (DCNN) and Transfer Learning have been successful to recognize emotion in video sequences. However, these approaches remain limited because it is difficult to model spatio-temporal interactions between video frames or identify salient features to improve accuracy. In this article, we propose a facial expression recognition system combining the representations of Deep Learning features and dynamic texture features. For the Deep Learning part, we used the VGG19 model to extract facial features, which will feed the LSTM (Long Short Term Memory) cells in order to extract spatio-temporal information between frames. While the HOG-TOP (Histogram of Oriented Gradients from Three Orthogonal Planes) descriptor aims to extract dynamic textures from video sequences to characterize facial appearance changes. Finally, we combine both models with a Multimodal Compact Bilinear (MCB) algorithm to produce a robust descriptor vector. Classification was performed using the SVM (Support Vector Machine) classifier to predict the emotion class. The experimental part was carried out based on the INTERFACE05 dataset that the accuracy of facial expression recognition was increased almost 1% by the fusion method (98.44%) than the baseline approach (97.75%). To summarize, the proposed method obtain a higher accuracy and robust detection meaning.

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Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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  1. LISAC, Department of Computer Science, Sciences Faculty Dhar-Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Hajar Chouhayebi, Mohamed Adnane Mahraz, Jamal Riffi & Hamid Tairi

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Chouhayebi, H., Mahraz, M.A., Riffi, J. et al. A dynamic fusion of features from deep learning and the HOG-TOP algorithm for facial expression recognition. Multimed Tools Appl 83, 32993–33017 (2024). https://doi.org/10.1007/s11042-023-16779-8

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