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.

引用

页数：6

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