3D Facial Expression Database Research Articles

Statistical Learning of Facial Expressions Improves Realism of Animated Avatar Faces

A high realism of avatars is beneficial for virtual reality experiences such as avatar-mediated communication and embodiment. Previous work, however, suggested that the usage of realistic virtual faces can lead to unexpected and undesired effects, including phenomena like the uncanny valley. This work investigates the role of photographic and behavioral realism of avatars with animated facial expressions on perceived realism and congruence ratings. More specifically, we examine ratings of photographic and behavioral realism and their mismatch in differently created avatar faces. Furthermore, we utilize these avatars to investigate the effect of behavioral realism on perceived congruence between video-recorded physical person’s expressions and their imitations by the avatar. We compared two types of avatars, both with four identities that were created from the same facial photographs. The first type of avatars contains expressions that were designed by an artistic expert. The second type contains expressions that were statistically learned from a 3D facial expression database. Our results show that the avatars containing learned facial expressions were rated more photographically and behaviorally realistic and possessed a lower mismatch between the two dimensions. They were also perceived as more congruent to the video-recorded physical person’s expressions. We discuss our findings and the potential benefit of avatars with learned facial expressions for experiences in virtual reality and future research on enfacement.

SIAT-3DFE: A High-Resolution 3D Facial Expression Dataset

3D facial expression dataset plays an essential role in computer vision and computer graphics, especially to the data-driven machine learning algorithms like deep-learning etc. In contrast with traditional low-resolution and low-accuracy 3D face related datasets, an accurate and dense facial expression database was introduced in this paper. During the period from January to June 2019, we have collected 8,000 3D facial expression models and 32,000 texture images from 500 subjects. The 3D facial capture device works based on structured light principle. The procedure of 3D face capture and property of 3D samples are introduced in details. The proposed high-accuracy 3D face dataset can be used in many 3D applications, like recognition, animation, and 3D facial expression simulations.

Análisis y Comparación del Descriptor Cone Curvature frente al Reconocimiento de Expresiones Faciales

Se presenta el resultado de analizar el comportamiento del descriptor de forma Cone Curvature (CC) en la tarea de reconocimiento de expresiones faciales en imágenes 3D. El descriptor CC es una representación del modelo 3D que se calcula a partir de un conjunto de ondas de modelado para cada vértice de una malla poligonal. Se empleó la base de datos de rostros 3D (BU-3DFE), la cual contiene imágenes con 6 expresiones faciales. Con el uso del descriptor CC, las expresiones fueron reconocidas en un porcentaje promedio del 76.67% con una red neuronal, y del 78.88% con un clasificador bayesiano. Al realizar una combinación del descriptor CC con otros descriptores como DESIRE y Spherical Spin Image, se logr´o un porcentaje promedio de reconocimiento de gestos del 90.27% y del 97.2 %, usando los mismos clasificadores mencionados previamente.

Feature selection for improved 3D facial expression recognition

Automatic recognition of facial movements and expressions with high recognition rates is essential for human computer interaction. In this paper, we propose a feature selection procedure for improved facial expression recognition utilizing 3-Dimensional (3D) geometrical facial feature point positions. The proposed method classifies expressions in six basic emotional categories which are anger, disgust, fear, happiness, sadness and surprise. The most discriminative features are selected by the proposed method based on entropy changes during expression deformations of the face. Developed system uses Support Vector Machine (SVM) classifier organized in two levels. The system performance is evaluated on 3D facial expression database, BU-3DFE. The experimental results on classification performance are superior or comparable with the results of the recent methods available in the literature.

Entropy-based feature selection for improved 3D facial expression recognition

Facial expressions contain most of the information on human face which is essential for human–computer interaction. Development of robust algorithms for automatic recognition of facial expressions with high recognition rates has been a challenge for the last 10 years. In this paper, we propose a novel feature selection procedure which recognizes basic facial expressions with high recognition rates by utilizing three-Dimensional (3D) geometrical facial feature positions. The paper presents a system of classifying expressions in one of the six basic emotional categories which are anger, disgust, fear, happiness, sadness, and surprise. The paper contributes on feature selections for each expression independently and achieves high recognition rates with the proposed geometric facial features selected for each expression. The novel feature selection procedure is entropy based, and it is employed independently for each of the six basic expressions. The system’s performance is evaluated using the 3D facial expression database, BU-3DFE. Experimental results show that the proposed method outperforms the latest methods reported in the literature.

Application of NSGA-II to feature selection for facial expression recognition

Facial expression recognition generally requires that faces be described in terms of a set of measurable features. The selection and quality of the features representing each face have a considerable bearing on the success of subsequent facial expression classification. Feature selection is the process of choosing a subset of features in order to increase classifier efficiency and allow higher classification accuracy. Many current dimensionality reduction techniques, used for facial expression recognition, involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. In this paper, we present a methodology for the selection of features that uses nondominated sorting genetic algorithm-II (NSGA-II), which is one of the latest genetic algorithms developed for resolving problems with multiobjective approach with high accuracy. In the proposed feature selection process, NSGA-II optimizes a vector of feature weights, which increases the discrimination, by means of class separation. The proposed methodology is evaluated using 3D facial expression database BU-3DFE. Classification results validates the effectiveness and the flexibility of the proposed approach when compared with results reported in the literature using the same experimental settings.