Overview of Facial Technology:
Facial technology is a biometric identification process to identify, verify, and authenticate the person using facial features from any photo or video. Facial recognition system works on comparing facial biometric patterns of the face of interest with the database of known faces to find the match.
How Does Facial 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.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.
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.
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.