Detection and Classification of Gender-Type Using Convolution Neural Network

Download Edit this record How to cite View on PhilPapers
Deep learning has a vital role in computer vision to discover things. Deep learning techniques, especially convolutional neural networks, are being exploited in identifying and extracting relevant features of a specific set of images. In this research we suggested that it could help in detecting the gender-type of individuals and classifying them using convolutional neural networks, as it achieved superior predictive performance in classifying individuals according to gender, and the experimental results showed that the proposed system works accurately and efficiently, which gives an accuracy rate of 97.76%.
(categorize this paper)
PhilPapers/Archive ID
Upload history
Archival date: 2021-01-07
View other versions
Added to PP index

Total views
97 ( #44,931 of 63,273 )

Recent downloads (6 months)
63 ( #9,689 of 63,273 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.