Facial Expression Recognition Using Hyper-Complex Wavelet Scattering and Machine Learning Techniques

Human emotion recognition is an active research topic in analysing the emotional state of humans over the past few decades. It is still a challenging task in artificial intelligence and human–computer interaction due to its high intra-class variation. Facial emotion analysis achieved more appreciation in academic and commercial potential challenges mainly in the field of behaviour prediction and recommendation systems. This paper proposes a novel scattering approach for recognizing facial dynamics using image sequences. Initially, we extract the temporal information from the facial frame by applying a saliency map and hyper-complex Fourier transform (HFT). Later the extracted high-level features are fed to the scattering transform method and machine learning algorithms to classify the seven emotions from the MUG dataset. The performance of proposed wavelet scattering network was evaluated on four different machine learning algorithms and achieved a high rate of recognition accuracy in all classes. In the experimental results, K-NN exhibits the proposed architecture’s effectiveness with an accuracy rate of 97% for the MUG dataset, 95.7% for SVM, 93.7% for decision tree and 91.2% naive Bayes, respectively.