scholarly journals MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

2019 ◽  
Author(s):  
Ilke Cugu ◽  
Eren Sener ◽  
Emre Akbas
Keyword(s):  
Expression Recognition ◽  
Face Images

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.

Expression Recognition Based on Low Pixel Face Images

2020 ◽  
Vol 57 (10) ◽  
pp. 101008
Author(s):  
刘芾 Liu Fu ◽  
李茂军 Li Maojun ◽  
胡建文 Hu Jianwen ◽  
肖雨荷 Xiao Yuhe ◽  
齐战 Qi Zhan
Keyword(s):  
Expression Recognition ◽  
Face Images

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

Facial expression recognition on real world face images using intelligent techniques: A survey

Optik ◽  
2016 ◽  
Vol 127 (15) ◽  
pp. 6195-6203 ◽  
Author(s):  
Sajid Ali Khan ◽  
Ayyaz Hussain ◽  
Muhammad Usman
Keyword(s):  
Facial Expression ◽  
Real World ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Face Images

scholarly journals Non-frontal facial expression recognition based on salient facial patches

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bin Jiang ◽  
Qiuwen Zhang ◽  
Zuhe Li ◽  
Qinggang Wu ◽  
Huanlong Zhang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Performance ◽  
Expression Recognition ◽  
Facial Landmarks ◽  
Face Images ◽  
Local Feature Extraction ◽  
Head Rotations ◽  
Part Model

AbstractMethods using salient facial patches (SFPs) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and they do not consider head position variations. We contend that SFP can be an effective approach for recognizing facial expressions under different head rotations. Accordingly, we propose an algorithm, called profile salient facial patches (PSFP), to achieve this objective. First, to detect facial landmarks and estimate head poses from profile face images, a tree-structured part model is used for pose-free landmark localization. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks while avoiding their overlap or the transcending of the actual face range. To analyze the PSFP recognition performance, three classical approaches for local feature extraction, specifically the histogram of oriented gradients (HOG), local binary pattern, and Gabor, were applied to extract profile facial expression features. Experimental results on the Radboud Faces Database show that PSFP with HOG features can achieve higher accuracies under most head rotations.

scholarly journals Non-frontal Facial Expression Recognition Based on Salient Facial Patches

2020 ◽  
Author(s):  
Bin Jiang ◽  
Qiuwen Zhang ◽  
Zuhe Li ◽  
Qinggang Wu ◽  
Huanlong Zhang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Good Choice ◽  
Expression Recognition ◽  
Facial Landmarks ◽  
Face Images ◽  
Local Feature Extraction ◽  
Head Rotations ◽  
Part Model

Abstract Methods using salient facial patches (SFP) play a significant role in research on facial expression recognition. However, most SFP methods use only frontal face images or videos for recognition, and do not consider variations of head position. In our view, SFP can also be a good choice to recognize facial expression under different head rotations, and thus we propose an algorithm for this purpose, called Profile Salient Facial Patches (PSFP). First, in order to detect the facial landmarks from profile face images, the tree-structured part model is used for pose-free landmark localization; this approach excels at detecting facial landmarks and estimating head poses. Second, to obtain the salient facial patches from profile face images, the facial patches are selected using the detected facial landmarks, while avoiding overlap with each other or going beyond the range of the actual face. For the purpose of analyzing the recognition performance of PSFP, three classical approaches for local feature extraction-histogram of oriented Gradients (HOG), local binary pattern (LBP), and Gabor were applied to extract profile facial expression features. Experimental results on radboud faces database show that PSFP with HOG features can achieve higher accuracies under the most head rotations.

scholarly journals A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanghong Liu ◽  
Jintao Liu
Keyword(s):  
Diffusion Equation ◽  
Face Detection ◽  
Anisotropic Diffusion ◽  
Three Dimensional ◽  
Face Image ◽  
Expression Recognition ◽  
Face Images ◽  
The Face ◽  
Video Recognition ◽  
Anisotropic Diffusion Equation

In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.

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