Application of Convolutional Neural Networks to Emotion Recognition for Robotic Arm Manipulation

被引：0
|
作者：
Fuertes, Walter [1 ]
Hunter, Karen [1 ]
Benitez, Diego S. [1 ]
Perez, Noel [1 ]
Grijalva, Felipe [1 ]
Baldeon-Calisto, Maria [2 ]
机构：
[1] Univ San Francisco Quito USFQ, Colegio Ciencias & Ingn El Politecn, Quito 170157, Ecuador
[2] Univ San Francisco Quito USFQ, Ingn Ind, CATENA SFQ, Quito 170157, Ecuador
来源：
2023 IEEE COLOMBIAN CONFERENCE ON APPLICATIONS OF COMPUTATIONAL INTELLIGENCE, COLCACI | 2023年
关键词：
emotion recognition; convolution neural networks; robotic arm control; EYES;
D O I：
10.1109/COLCACI59285.2023.10225880
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents the development of a system that operates a robotic arm to deliver an object based on the facial expression of a human standing in front of the robot, demonstrating real-time emotion recognition for physical Human-Robot Interaction. To achieve this, a convolutional neural network-based model was developed to identify emotions in real time. The robotic arm operation was implemented using an embedded NVidia Jetson Nano computer, a web camera, and OpenCV, ROS, and TensorFlow libraries. Using a 26.6k face photos data set from the emotion detection database, the built emotion detection model demonstrated an accuracy of 93.5% and an error of 6.5% during training and validation. The final real-time prototype had a testing accuracy of 94% with an error of 6%. This proof-of-concept shows that in the near future more advanced applications that harness user emotions may also be built.
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收藏
页数：6
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