Introduction
As time is passing by, there is a rapid advancement of technology in the fields of security, medicine, education, and many other. This increased development has changed many perceptions of how data is recorded and used in the future.
Advancements in technology, particularly in security and surveillance, have led to the rapid adoption of facial recognition technology. This technology, based on computer vision, enables the real-time detection and authentication of individuals. From unlocking phones to verifying employees in banks and tracking student attendance in schools, face recognition is revolutionizing various sectors with its ability to quickly and accurately identify people.
Acoording to Statista, The market size in the Facial Recognition market is projected to reach US$4.94bn in 2024
In this article, I’ll let you know about facial recognition how it works, and its application that is widely discussed and used in our daily lives and has a huge impact on it. So let’s begin.
What is Facial Recognition
Facial recognition is a computer vision approach that is used by many industries for biometric verifications of their staff, workers, and employees. It’s an advanced tactic to detect, verify, and confirm the individual identity. Facial recognition can be done in both real-time and through photos and recorded videos to detect persons and objects in it.
It works by comparing the input image of interest with entire faces available in the system’s database and matching features. Face recognition has several methods of working and algorithms to authenticate a person’s identity but their performance and efficiency may differ.Â
Facial recognition and face detection are frequently associated, but they are distinct processes and shouldn’t be confused. Face detection identifies the presence of a face in a digital image or video, but it doesn’t recognize or identify the individual. While Facial Recognition upgrades it by detecting and verifying each person by matching the face stored in the database.
For instance, automated cameras often use face detection to ensure subjects are within the frame before capturing an image, displaying a bounding box around detected faces.
Till now you have grasped the basic knowledge of facial recognition so let’s dive deep into its working and understand how it works extraordinarily.
How facial recognition works
You see people and are good at recognizing your family members, relatives, and friends because you are familiar with their facial features and faces. The machines also do so but first, they need to be trained with data, the data is stored in their databases this information can be later used for the recognition of persons.
Steps for Face Recognition
There are many face recognition models and algorithms that are used for face recognition But here I’ll discuss the basic steps through which faces are recognized in a system so let’s jump into steps
Face Detection
The process starts with the detection of human faces the system or camera detects the human face alone or in the crowd by making bounding boxes around the detected face. It is easy when the person is looking at the camera but now due to advanced systems face can be detected even if the person is not looking straight at the camera. There are many object detection algorithms used for face recognition like OpenCV, YOLOv3, and SSD.
Features Extractions
After the face is detected the system is trained with the help of Computer Vision and Machine learning algorithms to detect facial features and landmarks of the person from the captured like the distance between the eyes, distance from forehead to chin, the contour of lips, size of ears and many other these landmarks are called as nodal points.
Facial Representation
When the face is detected, features are extracted and nodal points are created they are fed into the system, and the system converts it into a numerical form with a unique feature vector. These numeric codes are referred to as Faceprints, like we have a unique fingerprint everyone has their unique face print that is being matched with the training dataset.
Face Matching
This is the final step called matching of the face. Your Faceprint is being matched with all the data codes available in the database of the system. The database has information of all the registered users if the system finds the exact match of the faceprint it returns all the detail of the person. The time consumed during the process will depend on the number of faces in the database.
Models and Algorithm
As we have discussed basic flow of facial recognition processes let’s discuss some of the algorithms that are being used in it. There are many deep learning models used for facial recognition like face ner, deep face, open face, and many others but here we will discuss the working of face net and deep face model.
Face Net
Face net is used for Facial Recognition algorithm that was developed and proposed by google in 2015. It is based on the face recognition benchmark dataset such as the Youtube face database. It used pre-trained data and third-party implementations to provide amazing results. It is widely used and provides the best results in survey-related projects involving research and testing surveys.
Deep Face
The deep face is a facial recognition model proposed by Facebook in 2014 for tagging people in images. It’s a hybrid model that works by combining Google Face Bet, and Facebook Deep Face all in one to provide the most accurate results. Due to its fast and accurate behavior, it is used by many systems to detect faces for security and many other reasons.
Conclusion
So that’s it, folks!! this was your basic guide to facial recognition and how it works hope it helps you out. In the next part of the article, I’ll try to recover some amazing ways in which face recognition is being used and helping people and provide them ease.
Here at Tezeract, we provide many AI services related to face recognition want to start your first face recognition project contact us we will collaborate with you and design something exciting and useful.
