A Vital Component In Today's Security & Commercial Landscapes

A face remains the most widely used way of identifying or authenticating a person. A photo of it is on most identification documents that we carry in our wallets. A lot of information can be provided from a person’s face, clothing, and appearance, and today a person’s face has become the epicentre of the most fascinating and promising evolving forensic technology – face recognition.

In the field of biometrics, perhaps nothing stirs up as much debate as face recognition. Using technology to identify or verify a person has always been something easily understood in theory. Heavy use of such technology in Hollywood blockbusters like Minority Report has certainly helped the technology gain widespread exposure among mainstream audiences.

While humans have always had the innate ability to recognise and distinguish between faces, computers only recently gained the same ability. It is still improving rapidly today, but from the time it was first worked on in laboratories in the 1960s, the technology has advanced by leaps and bounds. In 2006, a test of several face recognition algorithms by the National Institute of Standards and Technology (NIST) showed that machine recognition has improved tenfold since 2002 and a hundredfold since 1995 1. The best algorithms actually performed more accurately than most humans can manage.

That was in 2006. Since then, face recognition has been one of the most progressive technologies in the world, thanks in part to new methods of measuring up a face.

In 2017, NEC’s face recognition technology had an accuracy of 99.2 per cent when it comes to detecting the right person at an airport passenger gate. This was confirmed in NIST’s tests that were conducted to recognise one individual at a time as they walked through an area without stopping or acknowledging the camera.


In the years after the September 11 terrorist attacks, governments around the world have been deploying thousands of cameras to capture videos of street corners, common walkways and other places of interest.

The issue that many law enforcement agencies face now is not so much a lack of data but often too much of it. Opening the data flood gates without being prepared for the volume of data often means being drowned in it. Too much information can overwhelm rather than help

In the short time span that authorities have to, say, identify the Boston bomber in 2013, they will have had to look through thousands of images to find a person of interest.



If smart sensors bring loads of information to public agencies, then the idea of Big Data is to make sense of it by analysing for patterns and understanding the relationships between various items in a data set.

Abundant, on-tap storage and number crunching computing power have made this possible today. Yet, the idea that one can throw a pile of data into a machine and discover accurate trends and predictions from it is not only flawed but dangerous. It leads to assumptions and fallacies that can lead to poor decision making.

Data collected over the years – “long data” – can help in an assessment of how secure an installation is, based on threats and risks. Another area concerns digital crime. Big Data can provide advanced analytics of how, where and when hackers may attack a city’s critical cyber infrastructure. By providing forewarning, city authorities can be more proactive in preventing an attack.

Yet, Big Data is not the simple answer to many issues that city planners face. Often, the theory and the ground situation can be very different.

Government planners have to be wary of vendors keen to supply more computing hardware and software systems, and be more focused on a holistic, multi-disciplinary approach when it comes to adopting Big Data in their decision-making process.

One simple question to ask is whether a Big Data solution provider uses relevant data sets and big-enough sample sizes. Traditionally, statisticians follow these rules, and so should any data that is input into a system to find new trends and predictions.

Very often, despite the huge amounts of computing data and information available at hand, the most accurate results still require domain experts who know how to search for the right things in the right way. Without that, Big Data merely throws up random results, which can vary differently each time a test is run.

Where can governments find useful, relevant data? This is where information management and inter-agency collaboration systems come in. They make it easy to manage, resolve and make use of the data collected from each government agency.

With a smart strategy for handling data, governments will not find themselves overloaded with information, or using the wrong sets of information to base a Big Data query on. Trends and predictions will also tend to be more accurate, leading to better decision-making.



Data doesn’t just come from one source, or come in one direction. Much of the massive amounts of data in future will likely be collected by smart devices – machines – that talk to one another. The communications among these machines is now a key consideration in developing smart cities.

After all, machines are going to communicate a lot more among themselves in future, often without any human intervention. Today’s Internet servers already talk to one another all the time. In the physical world, this will become even more common.

Many smart sensors will be able to not just communicate to a central system but also connect to their peers. Like how insects act in a swarm, dozens, hundreds or even thousands of sensors can interact with one another to relay information, verify that data and ultimately present a coherent piece of information to human decision makers.

One application is in future cars and road systems. Already trials are being conducted in Europe and elsewhere, where cars can be outfitted with communication devices that announce their presence as well as “talk” to surrounding devices, such as those installed on the kerb or on a traffic light.

A very fast, split-second change in a situation, say, if a driver is running a red light, could trigger a warning in other drivers in the vicinity, so that they are warned of the imminent danger. Such systems can also help alleviate jams. Each device could, for example, connect to one another when many cars are stuck on a highway jam, to relay that information all the way back to drivers who might be heading towards the jam, so they may avoid it.

Such intelligent agents are expected to be common in future cars, and NEC is a major vendor involved in several trials of such smart cars in Europe. Though such technologies are still some years away from mature commercial rollouts, issues are being ironed out to bring the scenario to reality.

Communications among smart devices will impact many facets of urban life. Its effect will become more pronounced as cities become denser and interactions between not just humans but an increasing array of sensors and communications devices increase.

Among the potential issues that government agencies have to work out is that of identity management. They would have to ensure that each machine on the network is what it says it is, so there is no “fake” information being spread through a swarm of machines.

Solutions to guarantee authentication but also non-repudiation and privacy protection for vehicle- to-vehicle communications are being finalised mostly in Europe and US.



Among the “machines” that communicate to one another all the time would be ones that we put on our bodies as we go about our day. Once thought of as futuristic toys, wearable computers are a reality today, thanks to low-cost electronics and connectivity to on-demand resources on the Internet.

From sensors that track your evening run to new devices that provide a full augmented reality view of the physical world, wearable computers are set to play a key role in public safety in future.

Law enforcement and public safety officers can be fitted with body cameras that stream a real-time video feed to a nearby mobile station, which can then inform them over a wireless link what to look out for.

Many things have to work together for this to be an ideal scenario. For starters, wireless networks that feed information to the wearer have to be robust enough to handle real-time information.

A first responder to an emergency should not have to wait for data to download onto his wearable computer.

What about the recordings that law enforcement officers have stored on their wearable computers? How can the judicial system properly ensure that videos of a crime scene are correctly handled as forensic evidence? The answer could be in the shape of an information management system tailored to fit law enforcement requirements.

The biggest challenge is not just in the technology. Users have to be able to get used to the technology. Wearing a body camera that constantly feeds information may be a chore for some users, so the type of context-aware information that they receive has to be useful.



With so many non-human sensors, meters and even connected wearable devices “talking” to one another, the IoT network is one that has to be built to enable these conversations. This network has to be not just fast but real-time, and come with low latency.

This network may be parallel to the wireless networks we use now –3G, 4G or Wi-Fi – or perhaps a heterogeneous one that can check on the condition of each network and choose the best path through the various networks.

The data exchange may not always require lots of bandwidth, since the information can be a simple data point on wind speed or water level. However, the network has to be robust enough to handle millions of connections with little lag.

Increasingly, 5G is being touted as the future. With speeds topping 1Gbps and latency as low as 1 millisecond, it is seen by many as the ideal connection for future IoT devices. A person may even remotely “drive” a vehicle or operate an excavator with the near-instantaneous reaction time.

Other wireless technologies such as NB-IoT (narrow-band IoT) are already being rolled out around the world. Specifically catering to linking up low-latency, low-bandwidth devices, they enable cities to be connected without jostling for a lane on the cellular networks that are often congested by human users.

In implementing a plan for the Internet of Things, governments have to consider the effects of data leakage and viral attacks. And in devising an information management strategy, they have to balance the effectiveness and speed of the network, against the security required to check data packets that are passed through the network.



Car owners may at be at the wheel in the future, but many will not have to place their hands on the steering wheel or even stepping on the brakes. Autonomous vehicles, now possible thanks to the advancement of AI, will one day be as common as vehicles driven by humans. That one day, according to car makers from Tesla to Volvo, could be less than 10 years.

Already, many trials have been carried out for autonomous vehicles, from buses to taxis, around the world. In December 2016, the first self-driving Uber cars started operating in San Francisco. These Volvo XC90 vehicles came with all the sensors needed onboard and there was still a safety driver inside the vehicle to take over when needed.

However, fully autonomous vehicles will be a few years away, at least. The main reason is safety. In 2016, a man driving a Tesla car was killed in an accident after ignoring warnings to take over from the autonomous system. Since then, autonomous car makers have doubled down on their efforts to make these vehicles even safer.

In a city of the future, autonomous cars have clear benefits. Unlike humans who may be tired after a day of work and lose concentration, AI does not. At the same time, it can automatically find the fastest, safest routes to a destination. This helps to reduce city-wide congestion.

In fast-expanding cities struggling to cope with transporting millions of commuters each day, autonomous vehicles will make a difference by increasing the availability of public transport. Bus drivers may have to be rotated on shifts, but AI does not tire. This enables them to continue running a public transport service efficiently – once the kinks with autonomous vehicles are ironed out in the years ahead.



In the 1980s, robots came to the fore for their efficiency on the manufacturing floor. Today’s robots are not only used to make goods more efficiently, they can serve a wider variety of roles.

For example, they can be used in cities where there is labour shortage. Instead of having waiters attend to every customer need at a restaurant, robots may be able to do the simple tasks. These include collecting the used cutlery and cleaning a table after a customer has finished his meal.

Robots can play more interactive roles as well. Socially intelligent robots may be placed to help people at airport information counters, to direct them to the right locations. They may usher travellers to the right queue at an airport immigration counter, freeing up humans to take on more challenging tasks.

Much of the intelligence could be in the form of software as well. Chatbots, now used in many websites, enable service providers to easily answer commonly asked questions. They are already being deployed by town councils in Britain, as intelligent personal assistants, to apply logic and help resolve problems for residents.

In the future, these chatbots could possibly perform transactions as well. This means they can answer personalised questions, for example, by helping a person apply for a permit or license with a government agency. As the software matures, there is no limiting the physical tasks that a robot that carry out as well.

In a library of the future, a robot could be the all-knowing librarian that can not only answer tough questions, but go fetch the book that you are looking for. Robotics have come a long way over the decades and it is poised by the big leap in the coming years, thanks to the recent advancements in AI and data analytics.



In building safer, more liveable cities, planners will have to identify which of the key trends and issues are topmost on their agenda. This may be a time to consider the following actions, as plans are drawn up for the development of a safer city:

A. Conduct a quick audit of the systems and projects in place currently. This will bring clarity to the systems – or lack thereof – in place today, so planners have a clearer view of the areas that are lacking and also to ensure no overlap.

B. Set goals clearly. Define the areas that are practical and achievable with technologies that are emerging in the years ahead. Factors to consider include the density and terrain of a city. These may enhance or limit the rollout of some technologies.

C. Develop a long-term plan. As technology changes so quickly, and standards evolve with new players entering the market, the plan has to include enough flexibility for the inclusion of new advancements. Vendor lock-in has to be avoided.

One lesson which NEC has learnt over the decades helping shape future cities is that technology adoption has to do with more than just the latest technology.

The future holds much promise when it comes to innovations that are starting to promise safer city living. Yet, much of this requires deeper consideration. How will individuals take to the new technology, for example? Are the ways we use to test user acceptance still valid today, given the different aspirations of citizens everywhere?

Ultimately, city leaders who best understand the public sentiment are in the best position to answer those questions. A decision to deploy a technology could open up opportunities and impact thousands and perhaps even millions of citizens.

For more information, visit nec.com/safety