When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her.

There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second?

Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person.

The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera.

Distinguishing Humans From Animals And Statues

The AI system continuously analyzes video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak.

When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared.

We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest.

The AI system continuously analyses video streams from the surveillance cameras in its network, distinguishing, human faces from non-human objects
AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight

Protecting People’s Privacy With AI Face Search 

All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance.

Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight.

Face Search Versus Face Recognition

AI Face Search

Face Recognition

Quick search for a particular person in video footage

Identify everyone in video footage

Match detected face(s) in video stream to target face(s) in an uploaded image

Match detected face(s) in video stream to a database

Do not store faces and names in a database

Must have a database with ID info

Automatically protect privacy for GDPR compliance in public places

May require additional paperwork to comply with privacy regulations

Lightweight solution

Complex solution for large-scale deployment

Main use: locate persons of interest in a large area

Main use: identify a person who passes through a checkpoint

Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored

Advantages Of AI Face Search

Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera.

In summary, the advantages of Face Search:

  1. High efficiency: a target person can be located within a few seconds, which enables fast response time.
  2. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries.
  3. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills.
  4. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving.
  5. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras.
  6. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings.
Face Search can be used in many public environments to identify faces against past and real-time video recordings.
AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image

Common Use Cases Of AI Face Search

In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include:

  • Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit.
  • Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time.
  • School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient.

In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring.

By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun

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Is The Physical Security Industry Doing Enough To Prevent School Shootings?
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How Internet Of Things (IoT) Aids Facility Management And Physical Security
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As buildings become more complex and smarter, the age-old traditional maintenance methods that are based mostly on hands-on human monitoring are becoming more and more inadequate. Instead, the world is fast adopting building automation as a key component of smarter and more proactive maintenance strategies. The aim is to free up maintenance staff and give them time to focus on other tasks while machines monitor the different systems that work together to make the facility functional. Specifically, Internet of Things - or, IoT - enablement appears set to transform the way facility managers deliver service to building occupants. The trends are many and the possibilities are almost mind-boggling, from inventory management, to work scheduling and energy efficiency, the list goes on and on. Below, we look at a few ways in which IoT is being used for Facility Management and Security. 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On page 52 of this guide by the US Department of Energy, they state that a functional predictive maintenance program could yield up to 10 times ROI, reduce maintenance costs by 25% to 30%, and reduce downtime by 35% to 45% Along with fire suppression, IIoT is effectively monitoring a wide range of systems such as lighting, HVAC and security Remote Monitoring Of Facilities Physical inspections have been a critical condition for the success of conventional maintenance programs, even in hazardous environments. But, with the increasing emphasis on personnel safety, organizations want alternative solutions that allow staff to examine assets without being physically present. Facility managers and their team working in industries like manufacturing, oil and gas, and mining can relate with these constraints. And these industries can benefit greatly from deploying predictive maintenance solutions. For example, in the oil and gas industry, IIoT sensors can be used to monitor remote and highly critical assets. These sensors can be used on pipelines to detect anomalies (especially corrosion) and pass that information to supervisors for necessary action. By doing this, potential failures are quickly predicted to avoid often disastrous incidents. Managing Energy Consumption Sensors are also being embedded in building components and devices like HVAC systems, lights, doors, windows to understand energy consumption and proactively manage it. Facilities that use this technology could achieve substantial energy savings. In a press release by IT research and advisory company, Gartner, they stated that IoT can help reduce the cost of energy - as well as spatial management and building maintenance - by up to 30%. Looking at HVAC systems very closely, we see that they are a major source of energy usage in any building These sensors work by monitoring different conditions in the building and causing a power-saving action based on the data received. For instance, occupancy sensors can order lights to turn on when it senses motion in a room and then turn off the same lights when there is no presence there. That way, there is no need to wait for someone to remember to switch off the lights when they are not needed.   Another very common use is in HVAC monitoring. Looking at HVAC systems very closely, we see that they are a major source of energy usage in any building. So, the issue is how can one use IIoT to manage HVAC and possibly reduce their energy usage? Well, in its most common form, IoT-enabled HVAC works as a connection of sensors and thermostats that monitor factors like indoor air quality, temperature, and environmental changes then communicate with the rest of the HVAC equipment and make needed adjustments for occupants’ comfort. Not only that. IoT-enabled HVAC works as a connection of sensors and thermostats that monitor factors like indoor air quality, temperature, and environmental changes The Technology Can Be Configured To: Track energy consumption at different distribution points throughout the building. Track usage from the power source right down to the consumption point. Detect sudden voltage drops or spikes (usually an indication of some fault). These are essential benefits because HVAC units are notorious for consuming large amounts of energy when they are working inefficiently. Security And Access Control Smart surveillance is another important area of application for IoT in facilities management. It takes several forms such as the monitoring of life-saving systems like intruder or fire alarms, invisible barriers, and other safety installations. Facility managers are using IoT across different industries to obtain live information about potential emergency situations with a view to responding before the issue escalates. In such cases, quick detection of any strange activity is key because many of these installations have tangible negative effects when they fail or when they are intentionally sabotaged.Smart surveillance is another important area of application for IoT in facilities management Fortunately, the surveillance equipment can also be setup to send alerts to cellphones to aid emergency response or evacuation as the case may be. Smart surveillance is also priceless for monitoring the situation in partially or fully automated remote facilities (especially oil and gas installations and mines), and in hostile environments with critical equipment where humans cannot work for extended periods of time. If you are not yet using IoT in your facility, you may be wondering where to start from. To avoid getting overwhelmed, a good place to start would be to try a small-scale deployment of this technology then review its ROI and impact on your operations before adopting a more widespread IoT implementation. This way you can gradually scale up as you and your staff come to understand and adapt and to this new way of doing things.