An Emotional Interaction Robot with Facial Expression Recognition Realized on Raspberry Pi and STM32

2021 ◽  
pp. 1231-1240
Author(s):  
Yuxin Zhou ◽  
Wei Zhong ◽  
Zhaoqiang Li ◽  
Tianyi Zhang ◽  
Songrui Han ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Raspberry Pi ◽  
Expression Recognition ◽  
Emotional Interaction

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.

Raspberry Pi assisted facial expression recognition framework for smart security in law-enforcement services

2019 ◽  
Vol 479 ◽  
pp. 416-431 ◽  
Author(s):  
Muhammad Sajjad ◽  
Mansoor Nasir ◽  
Fath U Min Ullah ◽  
Khan Muhammad ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Law Enforcement ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Raspberry Pi ◽  
Expression Recognition

scholarly journals EfficientNet-Lite and Hybrid CNN-KNN Implementation for Facial Expression Recognition on Raspberry Pi

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohd Nadhir Ab Wahab ◽  
Anthony Tan Zhen Ren ◽  
Amril Nazir ◽  
Mohd Halim Mohd Noor ◽  
Muhammad Firdaus Akbar ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Raspberry Pi ◽  
Expression Recognition
Sign in / Sign up

Export Citation Format

Share Document