PROJECT OVERVIEW


SAFR

SAFR is a secure and accurate AI facial recognition platform, designed for RealNetworks.

SAFR - Overview

Overview

PROBLEM DEFINITION


How to protect properties from unwanted visitors?

Criminal activity, ranging from burglary and unauthorized access to vandalism, remains a persistent and growing challenge across society. In the U.S. alone, a burglary occurs every 30 seconds, according to the FBI. The average financial loss per incident is $2,661, not accounting for the irreplaceable sentimental value of stolen items or the emotional and physical risk to people present during a trespass.

While financial damage is important, the most critical concern is safety. Unauthorized access to sensitive or crowded environments can put human lives at risk, disrupt operations, and compromise critical assets and data.

This challenge extends far beyond private homes. Universities must secure open campuses without restricting accessibility. Corporate offices and data centers need to protect intellectual property and sensitive information. Retailers face theft and fraud, while financial institutions, airports, manufacturing facilities, and critical infrastructure must prevent breaches that could have large-scale consequences. Stadiums and arenas add another layer of complexity, where crowd safety and rapid threat detection are essential.

The problem, therefore, is not only how to prevent crime but how to proactively detect, manage, and respond to security threats across diverse, high-risk environments, without compromising efficiency, privacy, or user experience.

DESIGN PROCESS


Design process

Following the Double Diamond framework (Discover → Define → Develop → Deliver), I moved from understanding user needs to validating solutions through iterative prototyping and testing, ensuring a user-centered and scalable design.

Discover

• User interviews

• Personas

• Journey map

• Competitive analysis

Define

• HMW questions

Develop

• Wireframes

• User flows

• High-fidelity design

• High-fidelity prototype

Deliver

• Usability testing

• Final design

• Design handoff

DISCOVER


User interviews

I conducted in-person interviews with two participant groups: property visitors and security guards (three participants per group), from three industries: higher education, corporate offices, and retail. The goal was to understand their daily challenges and expectations around property access and security monitoring.

Property visitors

Participants expressed a clear need for a entry solution that eliminates the hassle of carrying access cards, keys, or remembering passcodes.

Security guards

Participants highlighted that constant video monitoring is time-consuming and repetitive. They wanted to spend less time watching surveillance videos and more time on high-impact responsibilities such as performing inspections, preventing security incidents, and reports.

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.

DEFINE


How might we

I created How Might We questions that helped us better align on the tasks and goals of both property visitors and security guards:

  1. How might we provide a safer and seamless experience of entering a room for property visitors?

  2. How might we provide a platform where monitoring video surveillance is automated so that security guards don’t have to manually do it and can focus on other duties?

DEVELOP


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.

DELIVER


Usability testing

In-person usability testing with 5 participants (security guards) revealed that navigated the web app prototype with ease, successfully locating key information and completing critical tasks. This resulted in a high task success rate. Tasks:

  1. Find the Building Entrance view

  2. Identify a visitor flagged as a threat

  3. Find detailed information about that threat

Final design

The web app streamlines security operations by automating door monitoring and surfacing key information about visitors and real-time alerts. Its simple and intuitive design ensures users can respond quickly and confidently.

Design system

To support long-term scalability, I created a design system that accommodates all current and anticipated use cases.

Accessibility

A dark interface is chosen to improve readability and reduce eye strain to support viewing in dark environments, which is the most common use case for security guards. It aligns with WCAG guidelines by ensuring sufficient contrast ratios between text and background to maintain accessibility for users with visual impairments.

Usability heuristics

  • Visibility of System Status

  • User Control and Freedom

  • Consistency

  • Error Prevention

  • Recognition Rather than Recall

  • Flexibility

  • Flexibility and Efficiency of Use

Challenges and trade-offs

Recognition with facial obstructions

SAFR’s computer vision was significantly improved in 2020 to recognize masked faces during the pandemic. However, it is not 100% accurate for faces with other obstructions, like sunglasses or objects.

This caused a design challenge: how to communicate errors for failed face scans and guide users to recover. The trade-off was using a single, generic error message for scans that fail due to any facial obstruction, simplifying the experience while covering multiple scenarios.

The images below show a preview of the design system, and demonstrate how the web app works within a school environment.

SAFR - Recognition

Recognition

SAFR - Camera

Camera

SAFR - Events

Events

SAFR - Face Recognition

Face Recognition

SOLUTION


Facial recognition ecosystem

An AI facial recognition platform for live video that uses advanced technologies like machine learning and computer vision to detect faces and unlock entrances in real time.

Offered through a quote-based enterprise model, it ensures flexible deployment across secure, large-scale environments.

It is completely unbiased, accurately recognizing all skin tones and 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.

Use cases

  • Higher Education

  • Corporate Offices

  • Retail

  • Financial Institutions

  • Airports

  • Manufacturing

  • Data Centers

  • Critical Infrastructure

  • Stadiums & Arenas

  • In addition to its software platform, RealNetworks also offers dedicated hardware solutions that work together to create a seamless, unified facial recognition ecosystem.

    • SAFR Scan (Access Control device)

    • SAFR Camera

  • 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.

How SAFR Works

IMPACT


KPIs

Metrics show that users (security guards) 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.

75%

incident reduction rate

87%

adoption rate

95%

engagement rate

How we measured success

  • Incident reduction rate (75%) was calculated by comparing the number of verified trespassing incidents recorded in the customer’s security logs before and after the launch of the SAFR video surveillance system. SAFR’s analytics provided the event data used to identify and validate incidents 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.

All usage-based metrics were tracked with Google Analytics.

PROJECT DETAILS


Project details

  • Role: Senior Product Designer, UX Researcher

  • Company: RealNetworks

  • Industry: AI, SaaS

  • Market: B2B

  • Team: Product Designer, Product Manager, Engineering

  • Frameworks: Double Diamond, Scrum

  • Tools:

    • Design: Figma, Miro

    • Project management: Jira, Confluence

    • Product analytics: Google Analytics

  • Years: 2018, 2020

  • Platform: Web

  • Link: SAFR.com