Face Recognition [What is it and how does it work?]
To prevent the use of digital identities for criminal purposes, facial recognition has become a widespread tool in many countries. The demand for facial recognition software is increasing every year, with the market expecting to grow by $7.7 billion by 2022, and a large portion of its current use is to identify and authenticate users.
What is Face Recognition?
Many people are becoming increasingly familiar with facial recognition technology due to unlocking features in new phones requiring face ID. In this use case, facial recognition is used to determine that the individual is the owner of the device and authorize access to the phone.
Facial recognition is a technology that can match a human face from a digital image or video, against a database of stored faces. Facial recognition generally uses biometrics to help identify facial features. This type of identification is helpful for various commercial and law enforcement applications. When it comes to digital authentication, facial recognition falls under the category of biometrics.
Facial Biometrics: How It Works
Biometrics are biological measurements or physical features that are used to identify individuals. Some common forms of biometrics include fingerprint mapping, retina scans, and facial biometrics. Facial biometrics works through:
- Face Detection: The camera detects and locates a face individually or present in a crowd
- Face Analysis: The image of the face is captured and analyzed. Most variants of facial biometric technology tend to rely on 2D images rather than 3D images. The software finds the geometry of an individual’s face, like the distance between their eyes, distance from forehead to chin, etc. Facial landmarks are identified to make it easier to distinguish a face in a database and a real live person.
- Converting the Image: The captured face image (analog information) is transformed into a set of digital information (data) depending on an individual’s facial features. The numerical code is known as a faceprint. Each individual has a unique faceprint, also known as Biometric Templates.
- Finding the match: A unique faceprint is compared to a database of other known faceprints. A correct match has been made if the unique faceprint is matched to a known one in the database. More recently Artificial Intelligence and Machine Learning have become the emerging technologies used in Face Recognition. A.I. models are trained with image recognition algorithms and training sets to be able to match faces with different poses and different lighting conditions. These models are usually based on Deep Learning and Convolutional Neural Networks
Compared to other types of biometric recognition, facial recognition is considered the most natural form of matching, because that is how humans can actually determine how one person can differentiate from another: by comparing facial features. When compared with other forms of biometric screenings, Face Recognition has the best User Experience. To recognize fingerprints. hand palms or a person’s iris, some part of the human body need to be purposely scanned by a special sensor. In order to implement face recognition, just a simple selfie of the user is needed.
The Purpose of Face Recognition Technology
The main objective of facial recognition is to identify individuals, whether individually or collectively. The number of false positives can vary, depending on the technology used for facial recognition. The best face identification algorithm has an error rate of 0.08%. Facial recognition systems that operate with liveness detection, have higher rates of accuracy.
There are various benefits of facial recognition, depending on the industry and application. It can be a convenient, safe, and hassle-free method to identify a person at a distance without any physical contact. Facial recognition, when used correctly, has led to increased security functions, decreased instances of crime, faster processing, and greater convenience for the public. There are however concerns about the introduction of bias into systems that rely on facial recognition. Most recently Facebook announced that it is stopping the use of facial recognition to recognize people on its platform.
Applications of Face Authentication Technology
There are various applications of face authentication technology for law enforcement and other commercial ventures. Law enforcement agencies in the U.S and globally are beginning to rely on facial recognition to find suspected criminals by adding mugshots and other photos to their database. Once added to the database, the facial images are scanned whenever the police department carries out a criminal search.
Mobile face recognition has also enabled officers to use smartphones and other portable smart devices to take a photo of a driver or pedestrians and compare it to face recognition databases to identify individuals accurately.
In airports, border control security now uses facial recognition in conjunction with biometric passports. It enables commuters to skip the regular long lines in favor of a quicker automated gate that uses facial recognition to match the traveler with their passport image.
Online banking has also received a substantial boost thanks to biometric functions, specifically facial authentication. Customers can opt to authorize transactions simply by looking at their phones or laptops instead of accepting and manually entering a one-time password (OTP) on their devices.
Facial recognition is also used for entertainment purposes. Snapchat, Instagram and other social media platforms recognize faces and make fun and creative filters for people to enjoy. It has led to widespread usage of specially created filters that detect your facial features and adapt accordingly.
The difference between Facial Recognition vs. Facial Authentication
"Face Authentication" is usually referred to when a 1:1 match is needed, such as when a person unlocks their phone or an app with their face using the mobile phone camera. The objective is to make sure that the person present is the same that had previously enrolled in the identity verification process.
"Face Recognition" is referred to when in a 1:n match type, when the need is for a system to look up into a database for a match of one specific face among many other faces. Face Recognition is already widely used, for example by the police in many States, to look for suspects in security footage of a crime scene and look for a match in the suspects database.
Facial Recognition Technology and Mobile Security
The integration of facial recognition technology with mobile security has been advantageous to consumers and mobile companies alike. Many people store critical information on their phones, and criminals can easily steal and hack the phones to gain this information.
With the introduction of facial recognition technology, many phone manufacturers are making face IDs a method of identification for users to access their phones. It ensures the protection of personal data and ensures that sensitive data is inaccessible to those with malicious intent.
Apple is considered a pioneer in facial recognition for smartphones. It enabled its users to unlock their phones using Face ID and access specific locked apps, and even pay for various services just by scanning their face with the phone front camera.
Snapchat, another phone app, has also utilized facial recognition to make fun and creative filters for people to enjoy. It has led to widespread usage of specially created filters that detect your facial features and adapt accordingly.
One of the most widespread uses of Face Authentication is to authenticate users to their own devices. In this case "face matching" and "liveness detection" are performed locally on the handset. Another emerging usage model is to perform Face Authentication on the server. This means that the biometric templates are stored on the server and the face matching operation is done on the server. This is mostly true in industries that require KYC (Know Your Customer) law and regulations. Some banks and financial institutions need to confirm the identity of the user directly through the face image stored on the server and not through the usage of their phone.
Facial recognition has led to a widespread evolution in many of our daily errands and activities. It has the potential to ultimately evolve the way we conduct transactions and other security activities in the future. As phone manufacturers continue to tweak and bring out new changes using facial authentication, people will be able to carry out a host of activities with their faces as the only component needed for authentication.