Artificial intelligence (AI) is an interrupter that will have a major impact on the security industry when it hooks up with the analytic data generated from security devices. These physical security devices are becoming more commonly found in a variety of vertical markets.
In the future, machines and computer systems will be able to reinvent themselves on their own as AI performs tasks usually requiring human intelligence. Some of the data AI process will be coming from security devices like video cameras and biometrics, which generate data that AI can learn.
An up-and-coming research area that is pushing new technology through deep learning, AI can bring together large amounts of data and dig deeper to predict better. Data generated through various sensors, for example, can communicate with each other while collecting various forms of data.
Today’s IP video cameras collect and record video streams that generate immense amounts of data. AI can take the captured video frames and comprehend everything it sees. The machines continually absorb and process data as they are updated and eventually could have enough intelligence to make decisions on its own.
A recently released market research report shows the market for machine learning growing at a rapid 44.1% compounded annual growth rate over the next 5 years, driven largely by the financial services sector.
Other technologies like drones and robots are being equipped with onboard AI. The growth of Internet of Things (IoT)-enabled analytics is another fertile area for the implementation of machine learning techniques.
Biometrics and AI in access control systems can collect huge amounts of data about the distinct characteristics of particular parts of the human body, such as a person’s face, iris, DNA, vein, or fingerprint. The information is transformed into a code understandable by the AI system.
A facial recognition biometric system analyzes the distance between the eyes, the width of the nose, the depth of the eye sockets, the shape of the cheekbones, and the length of the jawline. Voice biometrics measures the shape of the vocal tract and characteristics such as pitch, cadence, and tone. Fingerprint biometrics collects specific features of a fingerprint, such as the ridgeline patterns.
Behavioral biometrics is another machine deep-learning capability that relies on AI for identity like continuous authentication on mobile devices. Behavioral biometrics identifies people by how they interact with devices and online applications.
Using a variety of device sensors, thousands of behavioral patterns can be used to continuously authenticate users. These parameters include tap duration, swipe speed, fingerprint area, session duration, and device acceleration. Profiles are built to determine the user behavior against an entire population set. Behavioral biometric technologies can capture more than 2,000 parameters from a mobile device, including the way a person holds the phone, scrolls, toggles between fields, the pressure they use when they type, and how they respond to different stimuli that are presented in online applications.
One of the most commonly used algorithms for deep learning is neural networks. They are used in multiple applications like pattern recognition, optimization, and prediction of outcomes.
Advances in AI will continue to drive even more capabilities of this technology. The security industry will play a key role in the development of the approach.