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

Applied Sciences

Shibaprasad Sen¹  Soumyajit Saha¹  Ram Sarkar²  Manosij Ghosh²  Soulib Ghosh²  Zong Woo Geem³  Pawan Kumar Singh⁴ 

[1] Department of Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata 700150, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;Department of Energy IT, Gachon University, Seongnam 13120, Korea;Department of Information Technology, Jadavpur University, Kolkata 700106, India;

关键词: feature selection;    facial emotion recognition;    harmony search algorithm;    cosine similarity;    Pearson correlation coefficient;    local binary pattern (LBP);   

DOI  :  10.3390/app10082816

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown