Count number of Faces from an Image using OpenCV in Python

By Laxmi Narayan Sahu

My team built a model which is used to find and identify human faces in digital images. To build this model I used the OpenCV framework in Python.

Face detection is also known as Facial detection. It is a computer vision technology that is used to find and identify human faces in digital images.

OpenCV is a real-time Computer Vision framework written in C/C++. It is used in many image processing and computer vision tasks.

About the code/ Code Explanation:-

Step 1:- First We have Imported the OpenCV library.

Step 2:- Then we loaded the pre-trained face cascade which was trained by the trained data set.

Step 3:- Now we have to load the image. # Here We have the image in the same file but when a user uses then they have to provide the address of the image on which they want to count the no of faces.

Step 4:- Now we have to convert the image into grayscale. We need to convert the image to grayscale because of the dimensity reduction. Like in RGB, there are 3 color channels and 3 dimensions while grayscale has only one dimension which reduces the complexity.

Step 5:- Here, the model will start detecting the no of faces in the image.

Step 6:- We get the output, which is the no of faces in an image.

 

Where to use this MODEl:- COUNT NO OF FACES IN AN IMAGE

1. Social media platforms: Social media platforms can utilize face counting to provide statistics on the number of faces in photos or group selfies. This information can be used to enhance user engagement or provide interesting insights to users.

2. Event photography: Photographers or event organizers can use face counting to determine the number of people attending an event. It can help in estimating crowd sizes, evaluating event popularity, or managing event logistics.

3. Security and surveillance: Face counting can be employed in security systems to monitor crowded areas, such as airports, train stations, or stadiums. By counting the number of faces, security personnel can assess the density of crowds or detect any unusual or suspicious behavior.

4. Market research: Face counting can be used in retail stores or shopping malls to gather data on customer footfall. By analyzing the number of faces in different areas of a store, retailers can gain insights into customer behavior, optimize store layouts, or evaluate the effectiveness of marketing campaigns.

5. Traffic analysis: Face counting can be applied in traffic analysis to estimate the number of occupants in vehicles. This information can help in traffic management, urban planning, or transportation system optimization.

6. Human-computer interaction: Face counting can be integrated into interactive systems or devices to provide personalized experiences. For instance, an interactive display can adapt its content based on the number of faces detected to cater to the preferences or interests of the viewers.

These are a few examples of this we can implement in many areas.

 

The Output is present here:-

Output 1

Output 2