Facial Expression Recognition in the Wild Using Improved Dense Trajectories and Fisher Vector Encoding

2016 ◽  
Author(s):  
Sadaf Afshar ◽  
Albert Ali Salah
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Dense Trajectories ◽  
Fisher Vector ◽  
Fisher Vector Encoding ◽  
In The Wild

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Toward Unbiased Facial Expression Recognition in the Wild via Cross-Dataset Adaptation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 159172-159181
Author(s):  
Byungok Han ◽  
Woo-Han Yun ◽  
Jang-Hee Yoo ◽  
Won Hwa Kim
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
In The Wild

scholarly journals Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 131988-132001 ◽  
Author(s):  
Thanh-Hung Vo ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang ◽  
Soo-Hyung Kim
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Super Resolution ◽  
Expression Recognition ◽  
In The Wild

scholarly journals eXnet: An Efficient Approach for Emotion Recognition in the Wild

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1087
Author(s):  
Muhammad Naveed Riaz ◽  
Yao Shen ◽  
Muhammad Sohail ◽  
Minyi Guo
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Data Augmentation ◽  
Raspberry Pi ◽  
Expression Recognition ◽  
Extensive Evaluation ◽  
Benchmark Datasets ◽  
Augmentation Techniques ◽  
In The Wild

Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for real-time systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.

Fine-Grained Facial Expression Recognition in the Wild

2021 ◽  
Vol 16 ◽  
pp. 482-494
Author(s):  
Liqian Liang ◽  
Congyan Lang ◽  
Yidong Li ◽  
Songhe Feng ◽  
Jian Zhao
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Fine Grained ◽  
In The Wild
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