Open AccessSelf‐adaptive weighted synthesised local directional pattern integrating with sparse autoencoder for expression recognition based on improved multiple kernel learning strategy
Author(s) -
Du Lingshuang,
Wu Yongbo,
Hu Haifeng,
Wang Weixuan
Publication year - 2020
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5127
Subject(s) - pattern recognition (psychology) , artificial intelligence , autoencoder , computer science , discriminative model , kernel (algebra) , feature (linguistics) , feature learning , block (permutation group theory) , sparse approximation , margin (machine learning) , representation (politics) , mathematics , artificial neural network , machine learning , linguistics , philosophy , geometry , combinatorics , politics , political science , law
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.