Technology counts on AI to authenticate and identify people
- By Brian Baker
- Jun 01, 2021
Not long ago, artificial intelligence was viewed as
science fiction. Today, it routinely makes our
lives more secure and convenient. AI surrounds
us in our everyday lives. Online entertainment
providers use it to suggest movies, TV shows
and music we might enjoy. Retailers try to influence our current
buying decisions based on previous purchases. Chatbots help us
make appointments with service providers.
The security industry also deploys artificial intelligence in
many ways. Facial recognition counts on AI to authenticate and
identify people by the shapes of their faces and features. Robots
and drones patrol perimeters looking for anomalies, leaving
human officers free to handle other potential threats and events.
AI-based software checks feeds from central video monitoring
stations to filter out false alarms.
DIVING A LITTLE DEEPER INTO THE TECHNOLOGY
In recent years, the use of artificial intelligence and its
subsets, machine learning and deep learning, have increased
exponentially. AI technology enables computers to mimic
human intelligence using logic based on if-then rules and
decision trees. Statistic techniques used in machine learning
allows computers to improve at tasks with experience. Deep
learning enables networks to train themselves to perform tasks
such as speech and image recognition. There are two main ways
of working with these technologies – rule-based algorithms and
Rule-based algorithms have limitations. Even the most
experienced computer engineer can’t prepare for all potential
situations that might arise within a camera’s field of view or an
employee arrives at a building entrance with his face covered
with a mask and goggles. As a result, these algorithms offer
While it’s not accurate to say neural networks work like a
human brain, they are inspired by it. Neural-node networks are
computing systems that learn to perform tasks by considering
examples rather than being programmed with task-specific rules.
The machine-learning model memorizes its training data and
makes predictions based on specific sets of situations.
For instance, it only recognizes human activity if it matches
previous examples. That’s why training software to identify
human beings or vehicles reliably requires exposing the neural
network to millions of images.
The network makes predictions about each presented image
and is corrected by humans when it makes mistakes. Neural
nodes are layered, each analyzing an image element. A prediction
is made once the image passes through and is processed by the
Network accuracy improves until it outperforms other methods.
Over time, the network will reliably predict the presence of
humans and vehicles or whatever else it is trained to recognize.
What makes these networks so powerful is their ability to
generalize concepts they’ve learned and then apply them to
images they never before have seen.
An example I often use is that of a cat. Ask 10 people to think
of a feline and most likely, you’ll get 10 different answers based
on distinct breeds, sizes, fur colors and many other features.
However, all would recognize each person’s visualization as some
type of a cat.
Let’s take a look at an everyday use of deep learning to
understand better how it impacts the security industry. Video
monitoring center operators are exposed to hundreds or
thousands of alarm images per shift. Blowing leaves, lighting
changes or a spider building a web in front of a camera lens may
trigger a false alarm. Traditionally, 95% or more of incoming
alarms are false. Today’s deep learning networks can eliminate up
to 99% of false alarms.
Improved security is one result. By reducing the false
alarm noise, operators are less likely to miss genuine alerts.
Operators’ ability to focus on potentially criminal activity
reduces response time if law enforcement or security guards
must be dispatched.
Monitoring cameras for hours is a demanding job, made more
so by dealing with false alarms. False alarm reduction software
improves employee morale, reducing turnover in the process.
By focusing on true alarms, operators become more productive,
enabling a station to add more cameras or new customers without
hiring new employees.
The cloud-based AI software requires no hardware devices to
be installed at an end-user’s site. Future upgrades are managed
remotely by the service provider.
Predicting criminal behavior is likely the next big step in deep
learning video analytics. Neural networks use the same training
methods to learn actions likely to precede a crime. This is a big
step as the software must recognize humans and identify things
that people interact within their environment.
Tremendous advancements in computational power made
artificial intelligence and deep learning
possible. Now, these technologies’ highly
accurate decision-making enables us to do
things better and faster than before. It is
encouraging to know these platforms continue
and learn and improve over time.
This article originally appeared in the May June 2021 issue of Security Today.