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. If we talk about security and surveillance they have also changed their structure from ordinary security cameras to advance biometrics systems that can capture and monitor real-time data through biometric verification and real-time detection and recognition of people and objects. Let’s take a basic example, I am pretty much sure most of you unlock your phones by scanning your face. You just take your face in front of the camera, and boom!! It unlocks. This magic happens with face recognition technology.
Face Recognition is becoming a heated topic in today’s world due to its quick breakthrough using Computer Vision technology. Object Recognition is a method to detect, locate and recognize objects in a frame. Similarly, Face recognition is a process used for the biometric verification of people to identify, verify and authenticate a person’s identity through AI Analysis. It is widely used in many areas to detect and authenticate individual identity such as in banks to verify employees, biometric systems in airports, Public CCTV cameras, real-time attendance of students in schools, and many other areas.
In this article, I’ll let you know about what is face recognition and 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 Face Recognition
Face 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. Face 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.
Face recognition is often used with face detection but both differ from each other and should not be used interchangeably because Face detection of a face in a digital image or processed video detects the human in an image or photograph but is unable to recognize and identify the person. Face detection is a part of face recognition, the first step in facial recognition is the detection of a face within images or videos. We have seen many automatic cameras that tell us to be in the frame by making small bounding box around our faces and clicking a picture after the face is detected.
As we have read there is much significant difference between these two but they are still being interlinked and used interchangeably. The major difference is that face detection is used to detect the presence of a person within the desired frame while Face Recognition upgrades it by detecting and verifying each person by matching the face stored in the database.
Till now you have grasped the basic knowledge of face recognition so let’s dive deep into its working and understand how it works extraordinarily.
You see people and are good at recognizing your family member, relatives, and friends because you are familiar with their facial features and faces. The machine 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
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.
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. The human face has up to 80 nodal points
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.
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 Algorithms
As we have discussed basic flow or face recognition processes let’s discuss some of the algorithms that are being used in it. There are many deep learning models used for face 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 is used for Face Recognition System 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.
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.
So that’s it, folks!! this was your basic guide to face 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 If want to develop your first face recognition project contact us we will collaborate with you and design something exciting and useful.