Age_Gender_Prediction

Project Description

This repository contains Python code for a CNN based Age and Prediction solution.

Dataset Used

The Adience Dataset

The Adience dataset consists of 26,580 images of faces. A total of 2,284 subjects (people) were captured in this dataset. The dataset was then split into two gender classes (male and female) along with eight age groups; specifically: 0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, and 60+.

Languages and Libraries used

• Python3
• Tensorflow 2
• Sklearn
• Numpy
• Opencv

Citation

Adrian Rosebrock, DL4CV, PyImageSearch, accessed on 20 November 2020