Laplacian nonlinear logistic stepwise and gravitational deep neural classification for facial expression recognition

Abstract

Facial expression recognition is the paramount segment of non-verbal communication and one frequent procedure of human communication. However, different facial expressions and attaining accuracy remain major issues to be focused on. Laplacian Non-linear Logistic Regression and Gravitational Deep Learning (LNLR-GDL) for facial expression recognition is proposed to select righteous features from face image data, via feature selection to achieve high performance at minimum time. The proposed method is split into three sections, namely, preprocessing, feature selection, and classification. In the first section, preprocessing is conducted with the face recognition dataset where noise-reduced preprocessed face images are obtained by employing the Unsharp Masking Laplacian Non-linear Filter model. Second with the preprocessed face images, computationally efficient relevant features are selected using a Logistic Stepwise Regression-based feature selection model. Finally, the Gravitational Deep Neural Classification model is applied to the selected features for robust recognition of facial expressions. The proposed method is compared with existing methods using three evaluation metrics namely, facial expression recognition accuracy, facial expression recognition time, and PSNR. The obtained results demonstrate that the proposed LNLR-GDL method outperforms the state-of-the-art methods.