Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

Leonel Miranda; Daniel Jimenez; Diego Benitez; Noel Perez; Daniel Riofrio; Ricardo Flores Moyano

doi:10.1109/ROPEC55836.2022.10018719

Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

Leonel Miranda
, Daniel Jimenez
, Diego Benitez
, Noel Perez
, Daniel Riofrio
, Ricardo Flores Moyano

Universidad San Francisco de Quito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5% of accuracy and a loss of 3.5% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2% accuracy and a margin of error of 1.8% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.

Original language	English
Title of host publication	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665458924
DOIs	https://doi.org/10.1109/ROPEC55836.2022.10018719
State	Published - 2022
Event	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 - Ixtapa, Mexico Duration: 9 Nov 2022 → 11 Nov 2022

Publication series

Name	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

Conference

Conference	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
Country/Territory	Mexico
City	Ixtapa
Period	9/11/22 → 11/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Jetson nano
convolution neural networks
gender recognition
robotic arm

Access to Document

10.1109/ROPEC55836.2022.10018719

Cite this

Miranda, L., Jimenez, D., Benitez, D., Perez, N., Riofrio, D., & Moyano, R. F. (2022). Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks. In 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC55836.2022.10018719

Miranda, Leonel ; Jimenez, Daniel ; Benitez, Diego et al. / Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks. 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022).

@inproceedings{97b974706eb44aaeb769d9c454e7b220,

title = "Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks",

abstract = "This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5\% of accuracy and a loss of 3.5\% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2\% accuracy and a margin of error of 1.8\% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.",

keywords = "Jetson nano, convolution neural networks, gender recognition, robotic arm",

author = "Leonel Miranda and Daniel Jimenez and Diego Benitez and Noel Perez and Daniel Riofrio and Moyano, \{Ricardo Flores\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 ; Conference date: 09-11-2022 Through 11-11-2022",

year = "2022",

doi = "10.1109/ROPEC55836.2022.10018719",

language = "Ingl{\'e}s",

series = "2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022",

}

Miranda, L, Jimenez, D, Benitez, D , Perez, N , Riofrio, D & Moyano, RF 2022, Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks. in 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022, Ixtapa, Mexico, 9/11/22. https://doi.org/10.1109/ROPEC55836.2022.10018719

Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks. / Miranda, Leonel; Jimenez, Daniel; Benitez, Diego et al.
2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

AU - Miranda, Leonel

AU - Jimenez, Daniel

AU - Benitez, Diego

AU - Perez, Noel

AU - Riofrio, Daniel

AU - Moyano, Ricardo Flores

PY - 2022

Y1 - 2022

N2 - This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5% of accuracy and a loss of 3.5% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2% accuracy and a margin of error of 1.8% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.

AB - This paper presents the development of a system for controlling a robotic arm to deliver an object depending on the gender identity (male or female) of a human recognized in front of the robot to demonstrate essential gender identification-based applications for physical Human-Robot Interaction. For this, we developed a convolutional neural network-based model for identifying genders. With the recognition result, the control of the robotic arm with six degrees of freedom was implemented using a Jetson Nano embedded computer, OpenCV, ROS, and TensorFlow libraries. The developed gender identification model achieved a 96.5% of accuracy and a loss of 3.5% during training and validation using a gender database composed of 50K gender images. The final real-time prototype obtained a 98.2% accuracy and a margin of error of 1.8% during testing. This proof of concept indicates that more complex applications based on the gender of the user could also be developed in the future.

KW - Jetson nano

KW - convolution neural networks

KW - gender recognition

KW - robotic arm

UR - https://www.scopus.com/pages/publications/85147538358

U2 - 10.1109/ROPEC55836.2022.10018719

DO - 10.1109/ROPEC55836.2022.10018719

M3 - Contribución a la conferencia

AN - SCOPUS:85147538358

T3 - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

BT - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

Y2 - 9 November 2022 through 11 November 2022

ER -

Miranda L, Jimenez D, Benitez D , Perez N , Riofrio D , Moyano RF. Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks. In 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022). doi: 10.1109/ROPEC55836.2022.10018719

Robotic Arm Handling Based on Real-time Gender Recognition Using Convolutional Neural Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this