3D face expression recognition with ensemble deep learning exploring congruent features among expressions. (26th December 2021)

• Record Type: Journal Article
• Authors: Rajagopal, Suganya Devi; Ramachandran, Baskaran
• Is Part Of: Computational intelligence. Volume 38:Number 2(2022)
• Page Start: 345
• Page End: 365
• Publication Date: 2021-12-26
• Subjects: 3D face expression recognition -- deep learning -- ensemble learning; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12498
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Automatic and accurate 3D face expression categorization has been a challenging task in many computer vision applications. This article presents a novel approach named 3D face expression recognition with ensemble deep learning (3D‐FER‐EDL). The framework comprises three levels. Each of the first two levels gives preference to a subset of labels, considering the congruent features among expressions. Each 3D expressive face is represented as a CDS features vector, which combines corner, distance, and slope features. CDS feature vector is given as input to the first level learners. Expression probability vectors from the first level learners are jointly given as the input to the second level learners. The probability vectors from level one and two are given as the input to the third level, which is the meta classifier. The experiments are conducted on the Bosphorus database and the CASIA 3D face database to evaluate the effectiveness of the proposed approach.