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 analyses 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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ASSA ABLOY's Yale celebrates 175 years, smart locks and new partnerships
ASSA ABLOY's Yale celebrates 175 years, smart locks and new partnerships

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Smart home access control growth and the future of door security
Smart home access control growth and the future of door security

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A surprising 42% of those surveyed, for example, were unaware they could control a smart door lock from their phone. In fact, many leading smart door lock models offer this feature, delivered by Wi-Fi or Bluetooth and an app. Despite a wealth of features offered by the latest smart door locks — remote and location-based locking/unlocking; voice activation; timed access; emailed entry alerts; and integration with smart camera and lighting systems — most people still have limited knowledge of their capabilities.  Smart technology is increasingly becoming the new norm in terms of home security  Only 14% of survey respondents described themselves as “very familiar” with what a smart lock can do. Even though most of them probably use smart access control solutions at their workplace. Secure homes through smart technology Monitoring and security are not the only drivers for smart home adoption. We humans also love convenience, and modern living presents us with problems that smart home technology can solve. Ironically, given the report’s findings, it takes a smartphone to really unlock the convenient possibilities of smarter living. The device that’s “always to hand” is central to the newest generation of smart door locks.A smart door lock is a convenient way for a landlord or agency to offer round-the-clock check-in and check-out If homeowners wish to remotely manage property access for friends and family, many smart door locks oblige. You let in guests remotely, send them a virtual digital key, or provide a temporary or single-use PIN to unlock the door. It is just as easy to revoke a digital key, if you don’t want its owner to come around anymore. This is a significant improvement over sharing physical keys — or hiding one under the doormat. We cannot be totally sure where a metal key ends up and have no way to track or cancel it once it’s “out in the wild”. 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Bosch startup SAST addresses need for evolved solutions in security industry
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We wanted to make sure that SAST is able to underline its role as an industry standard platform across multiple players. SAST is open to get additional investors and is being setup as a startup in its own offices in Munich to foster the environment where speed and innovation can more easily take place. Having said that, several entities of the Bosch Group are very interesting partners for SAST. The SAST App Store will allow developers to build and market new applications, similar to today’s app stores for smartphone applications Q: Please explain your "value proposition" to the industry. Mangold-Takao: We will bring new innovations and possibilities to the security and safety industry by providing an open, secure and standardised Operating System for video security cameras, to also address pressing issues such as cyber security and data privacy concerns. Devices that run then with the SAST operating system will work with an application marketplace provided and operated by SAST. Integrators and users can then use these apps from this marketplace to deploy additional functionality on these devices. With our platform we will be able to build up a community of app developers, including the ones not yet developing for this industry who have expertise in computer vision and artificial intelligence. Q: It seems what you are doing has parallels with the Apple and Android "app" stores. How is your approach the same (and how is it different) than those approaches? We are setting up SAST as a user-centric company and involve selected users very early on in the process"Mangold-Takao: The approach is similar in the way that we plan to generate revenue by operating the application marketplace and thus participate in the app revenue. 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We will also run selected pilots with end-users throughout 2019 to ensure we have all partners involved early on. SAST sees the industry is hungry for more innovative solutions – with the retail vertical market a target for these solutions Q: What timeline do you foresee in terms of implementing these initiatives? Mangold-Takao: While we start with first app development programs and plan our first pilots already for this year, we are planning our commercial launch for end of 2019. Q: How does your new company relate to the new Open Security & Safety Alliance (OSSA)? Mangold-Takao: The Open Security and Safety Alliance has been working very closely with SAST over the past year, defining some important concepts and elements required. One of the most important elements is an open and standardised Operating System, specific to this industry, which will then bring forward new innovative technologies and solutions. 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