Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm

• Main Authors: Soumyajit Saha, Manosij Ghosh, Soulib Ghosh, Shibaprasad Sen, Pawan Kumar Singh, Zong Woo Geem, Ram Sarkar
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Applied Sciences
• Subjects: feature selection; facial emotion recognition; harmony search algorithm; cosine similarity; Pearson correlation coefficient; local binary pattern (LBP)
• Online Access: https://www.mdpi.com/2076-3417/10/8/2816

Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.