With increasing breaches of passwords and personal data, companies are turning to biometric technology to enhance their security. This guide will layout the basic types of biometric security measures, provide an overview of their advantages and disadvantages, and recommend the optimal security arrangement based on the technology’s current capabilities.
Adopting artificial intelligence (AI) has become a focus for many American business leaders. A report from Genesys indicates that 60% of American company leaders anticipate using AI or advanced automation systems to improve operations, processes, staffing, budgeting, efficiency or security.
Simultaneously, reports from cybersecurity firms indicate a massive increase in breaches of both personal data and passwords. Traditional security systems are under greater pressure than ever before, and many enterprises are turning to biometric verification as their frontline of defense.
Biometrics is grounded in the measurement and analysis of individual physical characteristics. Each individual has an entire set of unique biometric markers, and this has made biometric solutions appealing to organizations that have concerns about authentication and access control.
In a matter of seconds, computers can search through stored data to identify a person, authenticate their security privileges, and render decisions about whether to provide them access.
Though the field has a long history with criminal identification and locations like airports and military gases, but the trend is now picking up in everyday areas of life. For example, many smartphones now include biometric unlocking as a part of mobile security.
At the enterprise level, on-site solutions remain the most popular way to deploy biometric technologies. Mobile devices are having their biometrics moment too, however. The global biometrics systems industry hauled in about $21.8 billion USD, and mobile biometrics as a field brought in over $20 billion, according to the report.
How to Secure Your Enterprise With Biometrics
- Use fingerprint, hand geometry and vein recognition
- Implement iris recognition
- Consider voice recognition
- Use face recognition
Biometrics Technology Overview
Fundamentally, most biometric data falls into one of two categories: Data gleaned from a person's physical body or how their behaviors are reflected in the body.
Examples of physical biometrics include facial recognition, iris recognition, vein recognition, fingerprint recognition, and hand geometry. Behavioral biometrics include handwriting, walking patterns, gestures, and voice recognition.
1. Use Fingerprint, Hand Geometry and Vein Recognition
If you're familiar with any traditional forms of biometrics, it's likely to be fingerprint recognition. It is by far the most widely used biometric solution for computerized authentication, used to handle device authentication, unlocking doors, and even clocking in for work.
Unfortunately, fingerprint technology is vulnerable to both forging and hacking. It also presents problems for individuals who have worn papillary patterns, dermatological issues, and dry skin. Groups affected by these problems include manual laborers and the elderly.
An arguably older solution is to use the geometry of the hand as a biometric marker. It has the virtue of being inexpensive and simple, but the downside is that human hand shapes aren’t highly unique.
Vein pattern recognition gives new hope to this technology, though. It involves mapping the endpoints and bifurcations of veins in either a finger or the whole palm. Using this approach, an image can be captured and converted into a digital format for storage, retrieval, and matching.
The position of the veins beneath the skin, rather than on the surface, provides greater security than fingerprints do. It’s also faster and more convenient for end-users. On the downside, this process is typically more expensive than fingerprinting. However, Apple is considering using palm biometrics for future Apple Watches and other devices.
Very cold hands have what are sometimes called "dead fingers." Individuals who suffer from disorders like Raynaud's syndrome may have veins that are either difficult or impossible to find using finger vein pattern recognition. A further issue is that the technology isn't widely known or utilized yet.
2. Implement Iris Recognition
In biometrics, iris scanning measures unique patterns in the colored area of the eye. It is a contact-free and fast method that has earned a deserved reputation for accuracy. Iris scanning can be employed at long distances, and some solutions only require a glance for a use.
The downside to iris scanning is that it depends heavily on specific hardware components, whereas face and voice recognition systems are mostly software-based. Consequently, it's seen much less in the consumer sector.
3. Consider Voice Recognition
Voice-enabled technologies are currently having a moment in the consumer sphere, and voice recognition systems are developing rapidly in sophistication alongside them.
Compared to iris scanning, voice recognition is a cheap way to verify someone’s identity. The implementation process simply requires a microphone of sufficient quality and an application that has been expertly trained in classifying sounds.
There are many ways to build a voice template for a user. Frequency combinations and statistical characteristics of the voice are the most common.
The determining disadvantage of voice authentication is low accuracy, however. A person's voice can vary based on health, age, and mood, which presents many scenarios where a legitimate user's voice may not be recognized. Likewise, environmental noise poses practical issues.
4. Use Face Recognition
Facial recognition systems handle identification and verification using data stored in a person's face. The system captures and analyzes an image, making comparisons between the user’s facial details and those on file for them.
The market for facial recognition technologies is expected to grow from $3.2 billion USD in 2019 to $7 billion in 2024. Growth drivers include increased user numbers and government-backed data security initiatives. Mobile device usage is also contributing to this rise in popularity, as is a high demand for robust fraud detection and prevention technologies.
AI-based facial recognition also represents one of the bigger challenges and opportunities for organizations interested in security.
In most cases, biometric data is available to the public. User photos can be acquired from social networks, or, snapped in real life. Consequently, one of the biggest concerns remaining to be addressed is verifying the liveness of the user's face. Anti-spoofing technologies should be applied to verify that a live person is in front of the camera, rather than a still image.
Another solution is to compare the submitted photo with an official ID document verification.
Optical Character Recognition (OCR) is not an official biometric verification technology, but it’s useful as a tool for comparing the provided information with user documents.
How to Choose the Best Biometrics-Based Technology for Security Systems?
After describing facial, vocal, iris, and fingerprint technologies, the hanging question is: which approach is best? Unfortunately, no single solution has emerged to beat out the others.
Multimodal biometric verification is considered the best approach to satisfy industries where high accuracy and security are essential. This approach requires two biometric credentials to provide positive identification.
Machine learning technologies are invaluable in deploying many multimodal forms of biometrics. A number of libraries are available today to provide the machine learning component including:
- Tensorflow: an open-source framework for building and computing data flow graphs. It allows for the creation and training of neural networks of level of complexity.
- PyTorch: an open-source machine learning library used for computer vision and natural language processing. PyTorch is lighter to work with and better for fast prototype development, but Tensorflow is much better for production models and scalability, as it was built to be production ready.
- Scikit-learn: another software-based machine learning library for the Python programming language
- OpenCV: an open-source computer vision and machine learning software library
There’s also a software platform known as Docker that allows for quickly building, debugging, and deploying applications.
Consultants Are Your Friends
While these biometric solutions and machine learning facilitators may seem highly technical and overwhelming, there’s no rule that you have to figure out which one is best by yourself.
Many organizations partner with ML consultants experienced in developing machine learning applications. This can be helpful in determining your business goals and choosing the right platform for your system.
When evaluating consultant options, especially firms or people you aren’t familiar with, starting with a proof of concept project can be very advantageous. This will be a quick project that they can work with existing code and data to deliver on.