Introduction to Biometric Scanning
Both face recognition and fingerprint authentication are essential tools in modern authentication security systems, and both are generally preferred over passwords, but they have different strengths and weaknesses making them ideal for different situations.
For example, facial recognition is generally considered more user-friendly and convenient as it doesn’t require any physical contact with any hardware, making it convenient for users.
But, the fact that facial scanning can take place passively has also led some to believe it will have a negative impact on society.
In low-light conditions, face recognition technology is less reliable than fingerprint scanning, and if the target individual has changed their appearance significantly or if the image is taken from an angle, facial recognition is less effective.
Contrastingly, fingerprint authentication is a well-established technology that has been used in law enforcement and other applications for many years.
While not generally considered as convenient, fingerprint scanning is more reliable and accurate than face recognition technology.
However, on the downside, because the data used for fingerprint scanning is very simple, it is easier for an attacker to trick a fingerprint scanner to gain unauthorized access.
FAR (False Acceptance Rate) and FRR (False Rejection Rate) are two statistical measures used in biometric scanning to evaluate the accuracy of the system.
FAR is the probability that an unauthorized person will be incorrectly identified as an authorized user (or falsely accepted), while FRR represents the probability that an authorized user will be incorrectly rejected as unauthorized (or falsely rejected).
Biometric scanning systems use statistical analysis to calculate FAR and FRR and optimize the system’s accuracy to minimize the chances of unauthorized access or false rejection.
By setting appropriate thresholds for FAR and FRR, biometric scanning systems can balance the system’s security and convenience to provide reliable and effective identity verification.
When it comes to FAR and FRR, facial recognition and fingerprint scanning have different levels of accuracy. Although the accuracy of both facial recognition and fingerprint scanning systems can be affected by various factors, such as lighting conditions, the quality of the sensor, and the quality of the database used for comparison, fingerprint scanning generally has a lower FAR and FRR than facial recognition technology.
The unique patterns on a person’s fingers are highly specific, which makes it easier for the system to accurately recognize the person. This means that fingerprint scanning systems can typically achieve a very low FAR and FRR.
Facial recognition technology, on the other hand, is considered to have a higher FAR and FRR than fingerprint scanning technology. This is because facial recognition technology relies on analyzing facial features, which can be more variable and prone to change than fingerprint patterns.
As a result, facial recognition systems may be more susceptible to false positives and false negatives, which can lead to a higher FAR and FRR.
Overall, face recognition and fingerprint scanning technologies are not 100% foolproof. As biometric authentication technology such as fingerprint and facial recognition becomes more widespread, there is an increasing risk of cyber-attack.
Several research groups have developed techniques to break biometric authentication systems, and there have been several instances of successful attacks in recent years.
In 2019, Facebook AI Research (FAIR) developed technology to trick facial recognition-based authentication and in 2020, researchers at Cisco created fake fingerprints using a 3D printer and were able to bypass multiple fingerprint authentication systems with an 80% success rate.
The best approach to ensure that biometric authentication is used securely is to apply multiple layers of security, such as combining fingerprint authentication with other factors like passwords or PINs, to create a more robust and secure system.
Let’s take a deeper look at these two biometric authentication methods to compare them and understand them better.
How Facial Recognition Works
Facial recognition technology is a process of identifying or verifying the identity of an individual using their facial features, such as the distance between the eyes, the shape of the nose, and the contour of the jawline.
The two types of facial recognition technology are 2D and 3D facial recognition.
2D facial recognition relies on two-dimensional images, typically from photographs or video footage, to identify individuals. It uses algorithms to analyze the image and extract facial features, such as the distance between the eyes and the angle of the nose, to create a map that can be compared with other images. One of the limitations of 2D facial recognition technology is that it has a hard time recognizing if the target has changed their appearance, such as by wearing glasses or a hat.
3D facial recognition technology, on the other hand, uses three-dimensional images of the face to create a more accurate facial template. 3D images are created from cameras by capturing multiple images of an object or scene from different angles and using software to combine them into a 3D model.
This 3D model can capture depth information and recognize the contours of the face, making it more reliable when there are variations in lighting and pose. 3D facial recognition technology is also being used in the development of virtual reality and augmented reality applications.
Facial recognition technology is complex. It involves both computer vision and machine learning to analyze images and compare them. The process involves multiple stages, including face detection, feature extraction, and face matching.
First, the technology identifies and locates the face in an image or video stream, then the face is isolated and analyzed for key points such as eyes, nose, ears, and mouth. Face matching involves comparing the extracted facial features to a database of known faces or to an existing set of data from an individual to verify they belong to the same person.
2D and 3D facial recognition technology have their strengths and limitations, but both have valuable applications in security, surveillance, and authentication. The computer science behind facial recognition technology involves complex algorithms, computer vision, and machine learning techniques.
How Fingerprint Scanning Works
Fingerprint scanning technology analyzes the unique patterns on an individual’s fingers to verify their identity. The types of fingerprint scanning technology are optical, capacitive, and ultrasonic sensors.
Optical fingerprint scanners use a light source to capture an image of the finger’s surface and create a digital 2D representation of it. Capacitive fingerprint scanners use electricity to measure the ridges and valleys on a finger’s surface, creating a map of the fingerprint pattern. And, ultrasonic sensors use high-frequency sound waves to create a 3D image of the fingerprint.
One of the main advantages of fingerprint scanning technology is its accuracy and reliability. The patterns on each person’s fingers are highly unique, and the chance of two fingerprints being identical is very low.
Another major benefit of fingerprint scanning technology is that it is very easy to use. The target only needs to place their finger on the sensor for a few seconds.
On the downside, if fingerprint profile data is stolen in a cyber-attack, it can be used to authenticate as the the person and gain access to their account and information. Because people cannot change their fingerprints, it could compromise many people’s ability to login for their entire lifetime.
Although the same is potentially true for facial recognition, it is possible to use video facial recognition technology to verify that the sample being used to authenticate is a unique sample. Therefore, it is essential to ensure that fingerprint scanning systems are designed with appropriate security measures to prevent unauthorized access.
In recent years, there has been an increase in the use of artificial intelligence and machine learning algorithms in fingerprint scanning technology. These techniques can improve the accuracy of fingerprint recognition and reduce the chances of false positives and false negatives.
Additionally, mobile devices are increasingly incorporating in-display fingerprint scanners, which are more convenient for users than traditional fingerprint scanners.
There are several different types of fingerprint scanners, including optical, capacitive, and ultrasonic sensors, each with their strengths and limitations.
Optical fingerprint sensors use light to capture an image of the fingerprint’s surface. The sensor contains a light source that illuminates the finger, and a camera that captures an image of the reflected light. The image is then analyzed to create a digital representation of the fingerprint. Optical sensors are commonly used in mobile devices and are relatively inexpensive.
However, they can be less accurate than other types of sensors, particularly in low light conditions.
Capacitive fingerprint sensors use electricity to measure the ridges and valleys on the finger’s surface. The sensor contains electrical capacitors that generate an electrical field, and when a finger is placed on the sensor, the ridges and valleys of the fingerprint create variations in the electrical field.
These variations are used to create a digital representation of the fingerprint. Capacitive sensors are generally considered to be more accurate than optical sensors, but they can be more expensive.
Ultrasonic fingerprint sensors use high-frequency sound waves to create a 3D image of a fingerprint’s unique ridges and valleys. The sensor emits sound waves that bounce off the finger and create a 3D image of the fingerprint’s surface.
Ultrasonic sensors are more accurate than optical and capacitive sensors and can work in a wider range of conditions, such as when the finger is wet or dirty. However, they are also more expensive and less common in mobile devices.
The use of artificial intelligence and machine learning algorithms are also improving the accuracy of fingerprint recognition, and the development of in-display fingerprint scanners is making the technology more convenient for users.
On the downsite, fingerprint scanning comes with hygiene concerns. In situations where many people may use the same fingerprint scanner, such as in public places, there are real concerns about the spread of germs.
And, fingerprint scanning is more vulnerable to forgery or manipulation. High-quality fake fingerprints can be created using materials such as silicone or gelatin, allowing an attacker to bypass the authentication system.
If an attacker gains access to a database that stores fingerprint data, they could potentially use the stolen data for identity theft or other malicious purposes.
Finally, fingerprint scanning also has some limitations for certain individuals, such as those with certain medical conditions that affect their fingerprints.
Which Is Better?
Facial recognition and fingerprint scanning are two popular biometric authentication methods that have been widely used in various applications. While both methods have their strengths and weaknesses, there are some notable differences between them.
In general, facial recognition is considered more convenient than fingerprint scanning because the user does not have to do anything to interact with the scanner.
However, facial recognition has its limitations and can be less accurate in low light or when someone significantly changes their appearance.
In terms of accuracy, fingerprint scanning is generally considered to be more reliable than facial recognition technology. The patterns on each person’s fingers are so unique that fingerprint scanning is a highly accurate method of identification.
Facial recognition technology relies on analyzing complex features of the human face, such as the distance between the eyes, the shape of the nose, and the contour of the jawline, to create a digital representation of the face. This requires more complex data processing than fingerprint scanning.
Fingerprint scanning relies on data that is relatively simple compared to the complex features of the human face. This means that if an individual’s fingerprint data is stolen in a data breach, the security of their accounts could be more easily compromised than accounts protected by facial recognition.
Facial recognition-based authentication technology can still fall back on analyzing each submitted sample for uniqueness because there is exponentially more entropy.
The fact that there have been multiple breaches of fingerprint data also points to the fact that although it is more accurate in most situations, it is a higher risk technology than facial recognition.
At the end of the day, it’s fair to say that neither facial recognition or fingerprint authentication technologies should ever be used alone, but only as one part of a multi-factor authentication scheme.
