scholarly journals Facial Expression Recognition Using 3D Points Aware Deep Neural Network

2021 ◽  
Vol 38 (2) ◽  
pp. 321-330
Author(s):  
Imen Hamrouni Trimech ◽  
Ahmed Maalej ◽  
Najoua Essoukri Ben Amara
Keyword(s):  
Facial Expression ◽  
Point Cloud ◽  
Deep Neural Networks ◽  
Sampling Strategy ◽  
Unstructured Data ◽  
Expression Recognition ◽  
Geometric Information ◽  
3D Data ◽  
Point Set ◽  
3D Object Classification

Point cloud-based Deep Neural Networks (DNNs) have gained increasing attention as an insightful solution in the study field of geometric deep learning. Point set aware DNNs have proven capable of dealing with the unstructured data type and successful in 3D data applications such as 3D object classification, segmentation and recognition. On the other hand, two major challenges remain understudied when it comes to the use of point cloud-based DNNs for 3D facial expression (FE) recognition. The first challenge is the lack of large labelled 3D facial data. The second is how to obtain a point-based discriminative representation of 3D faces. To address the first issue, we suggest to enlarge the used dataset by generating synthetic 3D FEs. For the second one, we propose to apply a level-curve based sampling strategy in order to exploit crucial geometric information. The conducted experiments show promising results reaching 97.23% on the enlarged BU-3DFE dataset.

scholarly journals A SYSTEMATIC REVIEW OF METHODS OF EMOTION RECOGNITION BY FACIAL EXPRESSIONS

2021 ◽  
Vol 9 (5) ◽  
pp. 1141-1152
Author(s):  
Muazu Abdulwakil Auma ◽  
Eric Manzi ◽  
Jibril Aminu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
Scientific Research ◽  
Expression Recognition ◽  
Technical Literature ◽  
Research Results

Facial recognition is integral and essential in todays society, and the recognition of emotions based on facial expressions is already becoming more usual. This paper analytically provides an overview of the databases of video data of facial expressions and several approaches to recognizing emotions by facial expressions by including the three main image analysis stages, which are pre-processing, feature extraction, and classification. The paper presents approaches based on deep learning using deep neural networks and traditional means to recognizing human emotions based on visual facial features. The current results of some existing algorithms are presented. When reviewing scientific and technical literature, the focus was mainly on sources containing theoretical and research information of the methods under consideration and comparing traditional techniques and methods based on deep neural networks supported by experimental research. An analysis of scientific and technical literature describing methods and algorithms for analyzing and recognizing facial expressions and world scientific research results has shown that traditional methods of classifying facial expressions are inferior in speed and accuracy to artificial neural networks. This reviews main contributions provide a general understanding of modern approaches to facial expression recognition, which will allow new researchers to understand the main components and trends in facial expression recognition. A comparison of world scientific research results has shown that the combination of traditional approaches and approaches based on deep neural networks show better classification accuracy. However, the best classification methods are artificial neural networks.

scholarly journals 3D Facial Expression Recognition for Defining Users’ Inner Requirements—An Emotional Design Case Study

2019 ◽  
Vol 9 (11) ◽  
pp. 2218 ◽  
Author(s):  
Maria Grazia Violante ◽  
Federica Marcolin ◽  
Enrico Vezzetti ◽  
Luca Ulrich ◽  
Gianluca Billia ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Quality Function Deployment ◽  
Three Dimensional ◽  
Support Vector ◽  
Expression Recognition ◽  
Emotional Design ◽  
3D Data

This study proposes a novel quality function deployment (QFD) design methodology based on customers’ emotions conveyed by facial expressions. The current advances in pattern recognition related to face recognition techniques have fostered the cross-fertilization and pollination between this context and other fields, such as product design and human-computer interaction. In particular, the current technologies for monitoring human emotions have supported the birth of advanced emotional design techniques, whose main focus is to convey users’ emotional feedback into the design of novel products. As quality functional deployment aims at transforming the voice of customers into engineering features of a product, it appears to be an appropriate and promising nest in which to embed users’ emotional feedback with new emotional design methodologies, such as facial expression recognition. This way, the present methodology consists in interviewing the user and acquiring his/her face with a depth camera (allowing three-dimensional (3D) data), clustering the face information into different emotions with a support vector machine classificator, and assigning customers’ needs weights relying on the detected facial expressions. The proposed method has been applied to a case study in the context of agriculture and validated by a consortium. The approach appears sound and capable of collecting the unconscious feedback of the interviewee.

Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

2017 ◽  
Vol 9 (5) ◽  
pp. 597-610 ◽  
Author(s):  
Guihua Wen ◽  
Zhi Hou ◽  
Huihui Li ◽  
Danyang Li ◽  
Lijun Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
Expression Recognition

An ensemble approach of improved quantum inspired gravitational search algorithm and hybrid deep neural networks for computational optimization

2021 ◽  
pp. 2150100
Author(s):  
Yogesh Kumar ◽  
Shashi Kant Verma ◽  
Sandeep Sharma
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Deep Neural Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Computational Optimization ◽  
Ensemble Approach ◽  
Gravitational Search

In this paper, an autonomous ensemble approach of improved quantum inspired gravitational search algorithm (IQI-GSA) and hybrid deep neural networks (HDNN) is proposed for the optimization of computational problems. The IQI-GSA is a combinational variant of gravitational search algorithm (GSA) and quantum computing (QC). The improved variant enhances the diversity of mass collection for retaining the stochastic attributes and handling the local trapping of mass agents. Further, the hybrid deep neural network encompasses the convolutional and recurrent neural networks (HDCR-NN) which analyze the relational & temporal dependencies among the different computational components for optimization. The proposed ensemble approach is evaluated for the application of facial expression recognition by experimentation on Karolinska Directed Emotional Faces (KDEF) and Japanese Female Facial Expression (JAFFE) datasets. The experimentation evaluations evidently exhibit the outperformed recognition rate of the proposed ensemble approach in comparison with state-of-the-art techniques.

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