Project Overview
SAFR
SAFR is a secure and accurate AI facial recognition platform, designed for RealNetworks.
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RealNetworks is a provider of artificial intelligence and computer vision based products. The company was founded in 1994, and is based in Seattle, Washington, United States.
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Overview
Problem Definition
How to protect properties from unwanted visitors?
Criminal activity, especially burglary has always been a big problem and concern in our society, and is on the rise once again. According to FBI, a burglar strikes every 30 seconds in the US.
The average loss from a burglary is $2,661. Stolen items can’t always be replaced, as many of them hold an important sentimental value to us. But material damage, of course, is not the worst thing that can happen when someone trespasses.
Having said that, how do we prevent these activities and protect our properties and most importantly, human lives?
Process
Design thinking
With the design thinking process, the goal was to identify types of users, jobs-to-be-done, and latent needs.
Discovery
• User interviews
• Personas
• Journey map
• Competitive analysis
Ideation
• HMW questions
• User flows
• Wireframes
• High-fidelity design
Prototyping
• High-fidelity prototype
Testing
• Usability testing
Implementation
• Final design
• Design handoff
Discovery
User interviews
I conducted in-person interviews with two distinct groups of participants: property visitors and security guards. These were the findings.
Property visitors
Participants shared the need for a solution that would allow them to enter a physical space without the need to use a card, a key or a password.
Security guards
Due to a large scope of their work, the sentiment was that they would like to spend less time monitoring video surveillance because it’s way too time-consuming. They would like to be more productive, and focus on their other duties, such as performing inspections, preventing criminal activity and reporting incidents.
Personas
Given that are two types of users of this product, I created two personas based on user interviews that I’ve conducted, that showcase their specific pain points, tasks and goals.
Journey map
In this journey map, I built on the previously defined persona of a property visitor, mapping out their experience when visiting a school property.
Competitive analysis
While competitors with machine learning and computer vision capabilities offer most features considered for this project, their products usually just offer SDKs and APIs to their customers, without having a web app.
Ideation
How might we
I created how might we questions that helped us better align on user’s tasks and goals:
How might we provide a safer and seamless experience of entering a room for property visitors?
How might we provide a platform where monitoring video surveillance is automated so that security teams don’t have to manually do it and can focus on other duties?
Wireframes
When I started ideating, my main focus was information architecture. As pictured below on the Overview page, I was unsure of what information we should surface to users and where that information should appear.
User flow
In this phase, I explored the potential user flow of the solution, focusing on security guards and how they would likely want to achieve their goals.
Testing
Usability testing
In-person usability testing revealed that participants navigated the prototype with ease and were able to locate key information, such as Events and Camera, leading to a high task success rate.
Solution
Face detection and unlocking
AI facial recognition platform for live video that uses advanced technologies like machine learning and computer vision to detect faces and unlock important entrances in real-time. It is completely unbiased as it accurately recognizes all skin tones, genders, and even works seamlessly with face masks.
Unlike physical authentication methods that can be lost, misused, or stolen, face recognition offers optimal security and user convenience. The technology is proven to be 98.87% accurate.
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To ensure personal privacy, all enrolled and scanned biometric data is fully encrypted and does not contain any visual imagery of individuals’ faces. This helps to ensure that individuals identities are protected, avoiding any liability issues related to new and emerging privacy protection mandates.
Outcome
Final design
The functionality of the web app is built around user’s ability to monitor door entrances and act accordingly. Dark interface is chosen to improve readability and reduce eye strain to support viewing in dark environments which is the most common use case.
The images below demonstrate how the web app functions in a school setting.
Design system and accessibility
The functionality of the web app is built around user’s ability to monitor door entrances and act accordingly. Dark interface is chosen to improve readability and reduce eye strain to support viewing in dark environments which is the most common use case.
The images below demonstrate how the web app functions in a school setting.
Recognition
Camera
Events
Face Recognition
Result
Impact
Metrics show that users are actively engaging with the product and smoothly completing workflows. Additionally, reports indicate a reduction in trespassing incidents in properties where this technology is implemented.
90%
incident reduction rate
87%
adoption rate
95%
engagement rate
Metrics
Incident reduction rate (90%) was calculated by comparing the number of trespassing incidents before and after the launch of the video surveillance system, over time.
Adoption rate (87%) was calculated by the proportion of users who interacted with the app out of the total user base, since launch.
Engagement rate (95%) was calculated based on users who performed meaningful actions in the app, such as monitoring camera and events, per month.
Project Details
Project details
Role: Product Design, User Research
Company: RealNetworks
Team: Product Designer, Product Manager, Engineering
Framework: Agile
Tools:
Design: Figma, Miro
Project management: Jira, Confluence
Analytics: Google Analytics
Year: 2020
Platform: Web
Link: SAFR.com