Gender and Age Detection using OpenCV

Published on 14 Jun 2021. Written by Amitesh Kumar

This python project enables us to determine the gender and age of the people. Computer vision will help us to study the pattern and provides the result. But the views of computer limit itself to study the high-definition characteristics of human beings. The whole project is based on object recognition, video tracking, motion estimation, and image restoration. In this python project, we will use deep learning to identify the gender and age of the person.

Read more..

SLNOTE


Project Implementation

The output of the project is either in ‘Male’ or ‘Female’ in gender. The output of the project is around ranges in 0-2, 4-6, 8-12, 15-20, etc. in the age of that person. Although it is very difficult to guess the accurate age and gender of the person, through CNN architecture we can guess close results. The CNN architecture is convolutional neural network which has 3 layers that are:-

SLLATEST

• First layer; 96 nodes, kernel size 7
• Second layer; 256 nodes, kernel size 5
• Third layer; 384 nodes, kernel size 3

The objectives of the project are mainly to detect faces, classify into male/female, classify into one of the 8 age ranges then put the results in image and then display it. The dataset which is used for the project is OpenCV. The OpenCV is the open source computer which enables the system to recognize the images and patterns to give desired results. The system supports deep learning frameworks such as TensorFlow, Caffe, and Pytorch.

The developer can get the dataset for free from the internet. The images dataset has been collected from many albums which will be used to compare the user image. The dataset almost contains 27,000 photos in around 1 GB file size. Once we will install opencv-python header file in our python program, we are good to go. To get an image as argument from the user, the developer should use an argument parser. Initialize the protocol of buffer and model.

Mean values of ages and gender are also initialized to classify the data. ReadNet () method is used to hold the networks. To highlight the face and pause after a minute then it would need a wait key (), the function will enable us to return the value. If the value detected is 0 then that means it doesn’t contain any image. The shallow copy of the frame is created and gets its height, width.

Facebox is an empty list which will point the coordinates that the image is in. The image should be in the rectangle to let the system recognize it well. The same process is done for age gap also.

Conclusion

This project is useful in detecting faces and their age gaps in cameras. This application is also useful in detecting faces through a CCTV camera. This project is very feasible and affordable. It can easily be implemented and it is the best use of data analytics. For the project, one needs to possess a good knowledge of python languages and data manipulation.

SLDYK
Kit required to develop Gender and Age Detection using OpenCV:

• No Kit

Technologies you will learn by working on Gender and Age Detection using OpenCV:

• OpenCV
• Computer vision
• Python
• Image Processing

Any Questions?