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.
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 computer vision services and applications.
To create a simple face detection and recognition application we have three phases:
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:
-alan_grant [22 images]
-claire_dearing [53 images]
-ellie_sattler [31 images]
-ian_malcolm [41 images]
-john_hammond [36 images]
-owen_grady [35 images]
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.
After detecting the faces, the next phase is recognition. Under facial recognition, we have several frameworks, such as Openface, Facenet, VGGface2, and MobileNetV2, etc.
There are some steps for face recognition:
1. Data preprocessing
2. Facial feature extraction
3. Comparison of features between the target face and faces from datasets.
In data preprocessing, we can crop and resize face images and captured inside boxes. OpenCV library is used in this process.
Below the image shows how a face is detected, resized, and cropped.
Image Credits: www.luxand.com/facecrop
Now calculate the face embeddings to get facial features from the processed face images.
We will begin by loading the TensorFlow model. Next, we will define the network input, get the embeddings, and save to a pickle file and load our embedding dataset with corresponding labels. Then use Euclidean distance and a threshold to determine who each detected face belongs to.
Image Credits: www.pyimagesearch.com/2018/06/18/face-recognition-with-opencv-python-and-deep-learning/
As we welcome a new normal, artificial intelligence and its underlying technology are becoming a ubiquitous part of our everyday life. We, at Oodles, ensure that businesses and their customers receive optimum solutions with AI-led innovation, such as facial recognition.
Amid COVID-19 fears, facial recognition using OpenCV is emerging as an effective solution for enforcement of precautionary measures at shopping centers, corporate offices, and other public spaces.
The Oodles AI development team is constantly making efforts to build and deploy dynamic facial recognition systems, such as-
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