Age And Gender Detection Using Open CV

Abstract

This project presents a streamlined approach for 
multi-task learning in age and gender prediction 
from images, leveraging the capabilities of PyTorch 
and OpenCV. By integrating these technologies, the 
model offers a straightforward process for users to 
execute predictions by running the python script. 
Through this framework, users can efficiently 
harness the power of deep learning to simultaneously 
infer both age and gender from facial images, 
facilitating a wide range of applications across 
various domains such as demographic analysis, 
targeted 
marketing, 
and personalised user 
experiences. Age and gender detection is a crucial 
task in various fields such as demographic analysis, 
security systems, targeted advertising, and human
computer interaction. 
This project explores the development of a system 
capable of predicting age and gender from facial 
images using Python and Convolutional Neural 
Networks (CNNs). CNNs have been widely 
recognized for their ability to extract meaningful 
features from images, making them suitable for this 
task. The model is evaluated using standard metrics 
such as accuracy and Mean Absolute Error (MAE) 
for age prediction. The results demonstrate the 
effectiveness of deep learning approaches in 
demographic classification, achieving notable 
accuracy in real-world applications. 
This project serves as a foundation for further 
enhancements in precision, using more complex 
models or additional training data to improve predictions. The prediction will be in the form of 
categories where categories are a few age intervals  ike 0- 6,18-25, etc. The further goal of this project 
will be to predict the nearly exact age of the person 
.i.e , a single number rather than a range.Gender 
prediction – The prediction is a classifier based 
where categories are Male and Female.