FLEPNet: Feature Level Ensemble Parallel Network for Facial Expression Recognition

Mohan Karnati · Ayan Seal · Anis Yazidi · Ondřej Krejcar

79 citations0/0 files storedIEEE Transactions on Affective Computing

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

With the advent of deep learning, the research on facial expression recognition (FER) has received a lot of interest. Different deep convolutional neural network (DCNN) architectures have been developed for real-time and efficient FER. One of the challenges in FER is obtaining trustworthy features that are strongly associated with facial expression changes. Furthermore, traditional DCNNs for FER problems have two significant issues: insufficient training data, which leads to overfitting, and intra-class facial appearance variations. FLEPNet, a texture-based feature-level ensemble parallel network for FER, is proposed in this study and proved to solve the aforementioned problems. Our parallel network FLEPNet uses multi-scale convolutional and multi-scale residual block-based DCNN as building blocks. First, we consider modified homomorphic filtering to normalize the illumination effectively, which minimizes the intra-class difference. The deep networks are then protected against having insufficient training data by using texture analysis on face expression images to identify multiple attributes. Four texture features are extracted and combined with the image's original characteristics. Finally, the integrated features retrieved by two networks are used to classify seven facial expressions. Experimental results reveal that the proposed technique achieves an average accuracy of 0.9914, 0.9894, 0.9796, 0.8756, and 0.8072 on Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, Real-world Affective Face Database, and Facial Expression Recognition 2013 databases, respectively. Moreover, experimental outcomes depict significant reliability when compared to competing approaches.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2016Going deeper in facial expression recognition using deep neural networks 2020Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition 2018Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism 2018Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition 2017Identity-Aware Convolutional Neural Network for Facial Expression Recognition 2019Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure 2017Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

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