3D facial expression recognition using nonrigid CPD registration method

Trimech, Imen Hamrouni; Maalej, Ahmed; Amara, Najoua Essoukri Ben

doi:10.1109/setit.2016.7939917

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…In the approach proposed by Li et al, 11 they employed multiple kernel learning with histogram-based features. The approach presented by Trimech et al 38 is based on the registration method and used SVM for classification. The notable performance by Hariri and Farah 39 is due to the utilization of both the 3D data and the 2D depth images.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Rajagopal

Baskaran

2021

Computational Intelligence

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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.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Rajagopal

Baskaran

2021

Computational Intelligence

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Point-Based Deep Neural Network for 3D Facial Expression Recognition

Trimech

Maalej

Amara

2020

2020 International Conference on Cyberworlds (CW)

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Symmetric Shape Morphing for 3D Face and Head Modelling

Dai

Pears

Smith

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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We propose a shape template morphing approach suitable for any class of shapes that exhibits approximate reflective symmetry over some plane. The human face and full head are examples. A shape morphing algorithm that constrains all morphs to be symmetric is a form of deformation regulation. This mitigates undesirable effects seen in standard morphing algorithms that are not symmetry-aware, such as tangential sliding. Our method builds on the Coherent Point Drift (CPD) algorithm and is called Symmetry-aware CPD (SA-CPD). Global symmetric deformations are obtained by removal of asymmetric shear from CPD's global affine transformations. Symmetrised local deformations are then used to improve the symmetric template fit. These symmetric deformations are followed by Laplace-Beltrami regularized projection which allows the shape template to fit to any asymmetries in the raw shape data. The pipeline facilitates construction of statistical models that are readily factored into symmetrical and asymmetrical components. Evaluations demonstrate that SA-CPD mitigates tangential sliding problem in CPD and outperforms other competing shape morphing methods, in some cases substantially. 3D morphable models are constructed from over 1200 full head scans, and we evaluate the constructed models in terms of age and gender classification. The best performance, in the context of SVM classification, is achieved using the proposed SA-CPD deformation algorithm.

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3D facial expression recognition using nonrigid CPD registration method

Cited by 6 publications

References 9 publications

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Point-Based Deep Neural Network for 3D Facial Expression Recognition

Symmetric Shape Morphing for 3D Face and Head Modelling

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