scholarly journals Local feature extraction based facial emotion recognition: a survey

2020 ◽  
Vol 10 (4) ◽  
pp. 4080
Author(s):  
Khadija Slimani ◽  
Mohamed Kas ◽  
Youssef El Merabet ◽  
Yassine Ruichek ◽  
Rochdi Messoussi
Keyword(s):  
Facial Expression ◽  
Large Scale ◽  
State Of The Art ◽  
Research Work ◽  
Emotional Expressions ◽  
Expression Recognition ◽  
Technological Advancement ◽  
Recognition Systems ◽  
Local Feature Extraction ◽  
Micro Patterns

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.

scholarly journals Deep Temporal–Spatial Aggregation for Video-Based Facial Expression Recognition

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 52 ◽  
Author(s):  
Xianzhang Pan ◽  
Wenping Guo ◽  
Xiaoying Guo ◽  
Wenshu Li ◽  
Junjie Xu ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
State Of The Art ◽  
Spatial Aggregation ◽  
Expression Recognition ◽  
Temporal Features ◽  
Visual Descriptors ◽  
Feature Aggregation ◽  
Temporal Feature ◽  
Video Descriptor

The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based facial expression owing to the gap between the visual descriptors and the emotions. In order to bridge the gap, a new video descriptor for facial expression recognition is presented to aggregate spatial and temporal convolutional features across the entire extent of a video. The designed framework integrates a state-of-the-art 30 stream and has a trainable spatial–temporal feature aggregation layer. This framework is end-to-end trainable for video-based facial expression recognition. Thus, this framework can effectively avoid overfitting to the limited emotional video datasets, and the trainable strategy can learn to better represent an entire video. The different schemas for pooling spatial–temporal features are investigated, and the spatial and temporal streams are best aggregated by utilizing the proposed method. The extensive experiments on two public databases, BAUM-1s and eNTERFACE05, show that this framework has promising performance and outperforms the state-of-the-art strategies.

scholarly journals Frame-Based Facial Expression Recognition Using Geometrical Features

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Anwar Saeed ◽  
Ayoub Al-Hamadi ◽  
Robert Niese ◽  
Moftah Elzobi
Keyword(s):  
Facial Expression ◽  
Prior Knowledge ◽  
State Of The Art ◽  
Recognition Rate ◽  
Human Interaction ◽  
Expression Recognition ◽  
Acoustic Data ◽  
Geometrical Features ◽  
Benchmark Datasets ◽  
Neutral Expression

To improve the human-computer interaction (HCI) to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness), with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

scholarly journals Efficient Detection of Occlusion prior to Robust Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Rui Min ◽  
Abdenour Hadid ◽  
Jean-Luc Dugelay
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Video Surveillance ◽  
Real World ◽  
State Of The Art ◽  
Efficient Detection ◽  
Art Works ◽  
Enormous Amount ◽  
Recognition Systems ◽  
Robust Face Recognition

While there has been an enormous amount of research on face recognition under pose/illumination/expression changes and image degradations, problems caused by occlusions attracted relatively less attention. Facial occlusions, due, for example, to sunglasses, hat/cap, scarf, and beard, can significantly deteriorate performances of face recognition systems in uncontrolled environments such as video surveillance. The goal of this paper is to explore face recognition in the presence of partial occlusions, with emphasis on real-world scenarios (e.g., sunglasses and scarf). In this paper, we propose an efficient approach which consists of first analysing the presence of potential occlusion on a face and then conducting face recognition on the nonoccluded facial regions based on selective local Gabor binary patterns. Experiments demonstrate that the proposed method outperforms the state-of-the-art works including KLD-LGBPHS, S-LNMF, OA-LBP, and RSC. Furthermore, performances of the proposed approach are evaluated under illumination and extreme facial expression changes provide also significant results.

scholarly journals 3-Dimensional facial expression recognition in human using multi-points warping

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Olalekan Agbolade ◽  
Azree Nazri ◽  
Razali Yaakob ◽  
Abdul Azim Ghani ◽  
Yoke Kqueen Cheah
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
The State ◽  
Superior Performance ◽  
Reference Object ◽  
Expression Recognition ◽  
Localization Error ◽  
Template Mesh

Abstract Background Expression in H-sapiens plays a remarkable role when it comes to social communication. The identification of this expression by human beings is relatively easy and accurate. However, achieving the same result in 3D by machine remains a challenge in computer vision. This is due to the current challenges facing facial data acquisition in 3D; such as lack of homology and complex mathematical analysis for facial point digitization. This study proposes facial expression recognition in human with the application of Multi-points Warping for 3D facial landmark by building a template mesh as a reference object. This template mesh is thereby applied to each of the target mesh on Stirling/ESRC and Bosphorus datasets. The semi-landmarks are allowed to slide along tangents to the curves and surfaces until the bending energy between a template and a target form is minimal and localization error is assessed using Procrustes ANOVA. By using Principal Component Analysis (PCA) for feature selection, classification is done using Linear Discriminant Analysis (LDA). Result The localization error is validated on the two datasets with superior performance over the state-of-the-art methods and variation in the expression is visualized using Principal Components (PCs). The deformations show various expression regions in the faces. The results indicate that Sad expression has the lowest recognition accuracy on both datasets. The classifier achieved a recognition accuracy of 99.58 and 99.32% on Stirling/ESRC and Bosphorus, respectively. Conclusion The results demonstrate that the method is robust and in agreement with the state-of-the-art results.

scholarly journals Facial Expression Recognition Based on Auxiliary Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 227 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
State Of The Art ◽  
Face Image ◽  
Great Success ◽  
Expression Recognition ◽  
Input Information ◽  
The Face ◽  
Feature Information

In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.

scholarly journals Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

Sensors ◽  
2013 ◽  
Vol 13 (12) ◽  
pp. 16682-16713 ◽  
Author(s):  
Muhammad Siddiqi ◽  
Sungyoung Lee ◽  
Young-Koo Lee ◽  
Adil Khan ◽  
Phan Truc
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Recognition Systems ◽  
Human Facial Expression
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