Artificial intelligence (AI) is propelling a digital, technology-led future for humankind. The rapidly increasing pace of AI adoption is enabling businesses to enhance customer services and optimize backend operations with significant value. Facial recognition is one of the most widely adopted AI development services that is transforming the way businesses interact with customers. From employee attendance to contactless temperature scanner, facial recognition technology is making strides in the post-COVID world. In this article, we will go through a step-by-step guide to deploying facial recognition using OpenCV library.
Employing Computer Vision and OpenCV for Facial Recognition
In this article, you are going to learn how to perform face recognition through webcam. This project is done by using the computer vision library OpenCV. This library was discovered for supporting real-time ccomputer vision services
and applications.
To create a simple face detection and recognition application we have three phases:
Preparing Face Datasets
Data gathering is the main problem to build a face recognition application. It is difficult to collect a sample of one person manually. But if you have a limited dataset, then some different techniques we can use in this application for recognition are as follows:
1. Image Augmentation (create many images by one image)
2. One-shot learning (required only one image per person)
Below we demonstrate how datasets should be prepare per person:
-datasets
-alan_grant [22 images]
-claire_dearing [53 images]
-ellie_sattler [31 images]
-ian_malcolm [41 images]
-john_hammond [36 images]
-owen_grady [35 images]
Face Detection
An important phase of this application is face detection. The most common way to detect a face (or any objects), is using the Haar cascade classifier. It is the most effective object detection method, but you to trained it with a lot of images. Then Opencv comes with is detector and trainer feature, in this, you can train your own classifier for any object. If you don’t want to use your own classifier, then you can use a pre-trained classifier for face detection.
There are some other face detectors such as MTCNN, yoloface, and ultra-light face detector.
I found that yoloface is most efficient and has the highest accuracy.
Learn more: Deploying Facial Recognition Using OpenCV