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Mobile-Optimized Facial Expression Recognition Techniques
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DOI：10.1109/ACCESS.2021.3095844.png)
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摘要
En 中文
This paper presents two novel facial expression recognition techniques: the real-time ensemble for facial expression recognition (REFER) and the facial expression recognition network (FERNet). Both approaches can detect facial expressions from various poses, distances, angles, and resolutions, and both techniques exhibit high computational efficiency and portability. REFER outperforms the existing approaches in terms of cross-dataset accuracy, making it an ideal network to use on fresh data. FERNet is a compact convolutional neural network that uses both geometric and texture features to achieve up to 98% accuracy on the MUG dataset. Both approaches can process 14 frames per second (FPS) from a live video capture on a battery-powered Raspberry Pi 4.
Keyword:
Feature extraction
Face recognition
Neural networks
Real-time systems
Training
Memory management
Cameras
Facial expression recognition
machine learning
multithreaded
active shape model
pose-invariant
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Spatio-temporal convolutional features with nested LSTM for facial expression recognition基于时空卷积特征的嵌套LSTM人脸表情识别.png)
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NEUROCOMPUTINGIF6.5
PREAI
分享收藏Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks基于深度演化时空网络的人脸表情识别OAAI
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