AI Summary & Key Takeaways
AI InsightThe obtained results show that the complex wavelet transform together with sophisticated classifiers can serve as a powerful tool for facial expression recognition problem.
📌 3 Key Takeaways
Abstract
Moving from manually interaction with machines to automated systems, stressed on the importance of facial expression recognition for human computer interaction (HCI). In this article, an investigation and comparative study about the use of complex wavelet transforms for Facial Expression Recognition (FER) problem was conducted. Two complex wavelets were used as feature extractors; Gabor wavelets transform (GWT) and dual-tree complex wavelets transform (DT-CWT). Extracted feature vectors were fed to principal component analysis (PCA) or local binary patterns (LBP). Extensive experiments were carried out using three different databases, namely; JAFFE, CK and MUFE databases. For evaluation of the performance of the system, k-nearest neighbor (kNN), neural networks (NN) and support vector machines (SVM) classifiers were implemented. The obtained results show that the complex wavelet transform together with sophisticated classifiers can serve as a powerful tool for facial expression recognition problem.