Abstract
This research investigates the construction of a robust gender detection system using facial features and Convolutional Neural Networks (CNNs), exploring the impact of different layer configurations on accuracy and computational efficiency. With a validation accuracy of 91%, findings illuminate the nuanced relationship between precision and computational resources, enriching discussions on facial recognition technologies.
Faculty Advisor
Anas Hourani
Department/Program
Computer Science
Submission Type
in-person poster
Date
4-9-2024
Rights
Copyright the Author(s)
Recommended Citation
Ambrosio, Jose N T; Hourani, Anas; and Moy, Magdalene
(2024)
"Gender Detection in Facial Images: A Comprehensive CNN Analysis,"
SACAD: John Heinrichs Scholarly and Creative Activity Days: Vol. 2024, Article 63.
Available at:
https://scholars.fhsu.edu/sacad/vol2024/iss2024/63