Advancements in security & surveillance have changed the way data is captured and how to drive actions and make the best use of data in the future. Security systems can be as fundamental as the video camera to as complex as the biometric system to monitor, detect and record the intrusion. Today’s surveillance market has evolved and moved beyond these traditional cameras, and technologies like biometric facial recognition is taking centre-stage. The use of Machine Learning and Artificial Intelligence technologies empowers facial recognition to be the most effective contactless biometric system.
How Does Facial Recognition Technology Work?
Recognizing faces may seem a natural and easy-going process but creating the facial recognition technology from scratch is challenging. It is quite difficult to develop an algorithm which works well with varying conditions like large datasets, low illumination, pose variations, occlusion, varying poses, etc. Despite challenges during technology implementation, facial recognition technology is continuously increasing due to its non-invasive and contactless features.
So how does facial recognition system work? Technologies may vary, but here are the basic steps:
Step 1: Face Detection
The facial recognition process starts with the human face and the necessary facial features pattern of the person to be identified. When we think of a human face, we probably also think of the very basic set of features, which are eyes, nose, and mouth. Similarly, facial recognition technology also needs to learn what a face is and how it looks. This is done by using deep neural network & machine learning algorithms on a large database of images with human faces looking at different angles or positions.
The process starts with human eyes, which is one of the most accessible features to detect, and then it proceeds to detect eyebrows, nose, mouth, etc. by calculating the width of the nose, distance between the eyes, and the shape & size of mouth. Once it finds the facial region, multiple algorithm training is performed on large datasets to improve the algorithm’s accuracy to detect the faces and their positions.
Step 2: Feature Extraction
Once the face is detected, the software is trained with the help of computer vision algorithms to detect the facial landmarks (eyebrow corners, eyes gap, tip of the nose, mouth corners, etc.) Each landmark is considered as nodal points, and each face has approximately 80 nodal points. These landmarks are the key to distinguish each face present in the database.
After this, the registered face in the database is adjusted in position, size and scale to match with user’s face. It would help whenever the user’s face moves or expression changes; the software will accurately recognize it.
Step 3: Face Representation
When the facial feature is extracted, and landmarks, face position, orientation & all key elements are fed into the software, the software generates a unique feature vector for each face in the numeric form. These numeric codes are also called Faceprint, similar to Fingerprint in contact biometric system. Each code uniquely identifies the person among all the others in the training dataset. The feature vector is then used to search through the entire database of enrolled users during the face detection process.
Step 4: Face Matching
After generating the unique vector code, it is compared against the faces in the database. The database has all the information of registered users. If the software identifies the match for exact feature in the database, it provides all the person’s details. If the compared featured vector value is below a certain threshold value, the feature-based classifier returns the id of the match found in the database.
There are some challenges while performing the face matching process. If the image to be matched is in 3D format and the database image is also 3D, then matching will occur without any changes being made in image. If the database image is in 2D format and the image to be matched is 3D, then the matching process would take some 3D image changes. In 3D image, the facial expression and feature pattern will be different from the database image. So, once the facial landmarks are measured, a step-by-step algorithm is applied to the 3D image to get it converted into 2D image, which becomes ideal to be a potential match.
The process to compare one face to another face in the database or one-to-one mapping (1:1) is called Face Verification. But, if we compare one face to all the faces/ images from the database (1: N) to find the potential match, it’s called Face Identification.
In the COVID-19 outbreak, contact tracing through biometric identification has become the widely adopted tool to reduce the virus spread. From monitoring temperatures to identifying people without mask, various countries are including facial recognition into their systems and replacing it with contact biometric systems. It works on a large algorithmic scale, and the software stores or having access to abundance of data. As per the study, with nearly half of all American adults having their images are stored in one or more facial recognition databases used by various government agencies for public protection.
Use of artificial intelligence and machine learning technologies has made the facial recognition process carried out in real-time. The algorithm captures incoming 2D & 3D images depending upon device’s characteristics and analyses it using algorithmic scale without any error by matching it with the database image. The integration of smart technologies with high computing techniques makes the facial biometric system one of the safest and reliable online identity verification solutions.
PathPartner offers facial recognition solution based on machine learning and deep learning algorithms to identify people & analyse behavior. Our face recognition SDK is highly accurate and easy to integrate with any application encompassing a wide range of use cases. If you have any specific facial recognition application in mind tailored to your use-case? Please write to us at email@example.com