Self‐adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy. Issue 3 (29th January 2020)

• Record Type: Journal Article
• Authors: Du, Lingshuang; Wu, Yongbo; Hu, Haifeng; Wang, Weixuan
• Is Part Of: IET computer vision. Volume 14:Issue 3(2020)
• Page Start: 73
• Page End: 83
• Publication Date: 2020-01-29
• Subjects: image representation -- feature extraction -- image classification -- image texture -- face recognition -- learning (artificial intelligence); self-adaptive weighted synthesised local directional pattern -- sparse autoencoder -- improved multiple kernel learning strategy -- facial expression recognition tasks -- novel SW-SLDP feature descriptor -- facial images -- extracts sub-block features -- directional intensity contrast -- self-adaptive weights -- sub-block feature -- expressional image -- expressional texture information -- discriminative high-level feature -- feature representation -- IMKL strategy -- high-level features; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5127
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : This study presents a novel method for solving facial expression recognition (FER) tasks which uses a self‐adaptive weighted synthesised local directional pattern (SW‐SLDP) descriptor integrating sparse autoencoder (SA) features based on improved multiple kernel learning (IMKL) strategy. The authors' work includes three parts. Firstly, the authors propose a novel SW‐SLDP feature descriptor which divides the facial images into patches and extracts sub‐block features synthetically according to both distribution information and directional intensity contrast. Then self‐adaptive weights are assigned to each sub‐block feature according to the projection error between the expressional image and neutral image of each patch, which can highlight such areas containing more expressional texture information. Secondly, to extract a discriminative high‐level feature, they introduce SA for feature representation, which extracts the hidden layer representation including more comprehensive information. Finally, to combine the above two kinds of features, an IMKL strategy is developed by effectively integrating both soft margin learning and intrinsic local constraints, which is robust to noisy condition and thus improve the classification performance. Extensive experimental results indicate their model can achieve competitive or even better performance with existing representative FER methods.