Enterprises managing expansive digital libraries often spend hours searching for specific assets that feature a certain individual, whether for an influencer brand campaign or highlighting an employee in a staff newsletter. This is where AI-powered DAM platforms like Bynder come in, equipping users with facial recognition search to make it easy to find images of the exact person they’re looking for.
But what is facial recognition search, and how does it work in enterprise-grade DAM? Keep reading to find out ways you can leverage the power of AI facial recognition search to get the most out of your digital assets.
Key takeaways
- Face recognition in DAM automatically tags people in images – after a user manually tags someone once, the system will identify and tag that person in all past and future image uploads.
- There are several benefits of face recognition search capabilities – it saves time for DAM Admins, ensures consistent tagging, and improves people-featured content discoverability.
- It’s ideal for HR managers who need to manage employee photos, event managers who need to identify VIPs or speakers at events, or marketing managers who coordinate campaign assets involving brand ambassadors or celebrities.
What is facial recognition search in DAM?
Facial recognition search in DAM is an AI-driven technology that allows users to locate digital assets like images containing the face of a specific individual. By automatically tagging image assets when faces are identified, Bynder’s Face Recognition feature eliminates the need for time-consuming manual tagging. After a user tags a person in an image at least once, AI automatically tags them in new and existing images to ensure consistent tagging across their digital library.
The history of facial recognition search dates back to the 1960s when Woodrow Wilson Bledsoe classified photos of faces using a system of measurements that compared unknown faces to data points of previously uploaded photos.1 Fast forward sixty years, the latest facial recognition technology uses deep learning to analyze complex patterns to improve accuracy and efficiency. Today, facial recognition search is being used in various aspects of our daily lives, such as unlocking our phones or organizing our photos. Along with personal uses, organizations are using facial recognition search for tasks like delivering marketing campaigns and managing employee data.
Let’s explore how enterprises are using facial recognition search to enhance asset discoverability in their DAM platforms in the next section.
How is facial recognition search integrated with DAM platforms?
Bynder AI makes it easy to find the right content quickly and accurately thanks to AI Search Experience capabilities like face recognition. With the Face Recognition solution, you can automatically identify, tag, and find images of specific people in your DAM, improving the organization and discoverability of people-centric content.
Here’s how Bynder’s Face Recognition works:
- Face detection: After upload, our DAM platform scans and indexes assets immediately to identify faces.
- Facial analysis: Next, our DAM platform analyzes distinct facial features to generate unique feature vectors, looking at biometric data like eye color, lip contour, cheekbone structure, and other facial features.
- Face matching and recognition: Finally, facial recognition search technology compares the feature vectors to stored patterns within your DAM to automatically identify or suggest potential matches. Once all faces are identified, the system suggests potential matches based on existing tagged individuals. You can confirm these suggestions and link faces to specific names, which creates a continual learning pattern that improves with accuracy over time as more images increase the chance that a future face is matched. By automatically adding metadata to your digital assets, you can enjoy instant searchability across your entire digital library.
Discover Bynder’s AI and automation features here.
Facial recognition search use cases
Facial recognition search is a powerful tool various teams within your organization can use. Take Sauber, for example. As a Formula 1 racing team, Sauber attends countless racing events where thousands of race-day photos are taken. Combing through these photos to find specific drivers for promotional materials and social media posts can take hours. By using Bynder’s facial recognition search, the Sauber team no longer has to manually review photos one by one. Instead, they can easily detect individuals in photos upon upload to discover and distribute images quickly to keep audiences engaged.
BDA Inc. is another example. With nearly 22,000 photos of employee headshots, event photos, and sports logos stored in Google Photos, it would’ve taken 108 hours to correctly tag and organize each asset. Bynder’s facial recognition search solution enabled the project to be delivered 27 times faster, making the process of accurately tagging employees and individuals manageable and scalable.
There are countless additional use cases of facial recognition search that various teams can benefit from, such as:
- HR teams looking for employee or executive images for awards, internal publications, and team documentation
- Event organizers managing images of VIPs, event attendees, speakers, and sponsor representatives
- Legal and compliance teams verifying permissioned use of celebrity or employee images
Learn how Sauber Group fuels omnichannel content experiences on race day with Bynder AI here
Benefits of facial recognition search in enterprise-grade DAM
Bynder’s AI-powered DAM platform makes it easy to search for assets using Face Recognition to improve workflows across your organization. Enterprises with vast digital libraries can now easily discover the assets they need when they need them, allowing for faster time to market. Here are the many benefits of using AI facial recognition search in enterprise-grade DAM.
Enhance content discoverability
Facial recognition search makes it easy to quickly find images of specific individuals by simply typing in their names or using a smart filter with names and using that to search for people-related images. Within seconds, you can find assets containing specific individuals across vast libraries, even when descriptions or file names are inconsistent or missing.
Reduce admin workload
When you upload assets containing people to your DAM, this feature automatically identifies faces. Once a face is detected, DAM admins will need to only tag an image once, and then Face Recognition will automatically tag existing and all newly uploaded assets featuring that person in the DAM. This automation reduces time-consuming manual tagging efforts while also maintaining consistent naming conventions across large asset libraries. As you continue to upload digital assets to your Bynder DAM platform, the greater the chance that a future face is matched. Users can confirm naming suggestions upon upload, which creates a continual learning pattern that improves accuracy over time, ensuring assets are correctly tagged and organized.
Improve content management
One of the benefits of Bynder’s Face Recognition feature is the ability to reduce errors and ensure consistent tagging across all digital assets. Consistent tagging across your library makes it easier to manage assets and maintain compliance by ensuring images with expiration dates are used correctly. This helps maintain a well-organized digital library that makes it easy to discover the content you need.
Discover how Lucid Motors leverages Bynder’s AI Search capabilities to speed up content operations here.
Increase content ROI
Large enterprises can have expansive digital libraries with thousands of assets. When not properly organized, it can be easy for valuable assets to go missing without reaching their full potential. Face Recognition makes it easier to find assets, allowing users to reuse them more often, which requires less spending on new content. Teams can quickly find assets for omnichannel marketing efforts to drive campaigns across social media, blogs, email, and ads, accelerating content production for faster time-to-market while boosting content ROI. Face Recognition also enables teams to become more efficient in managing assets and, therefore, spend less time. This way, they can focus on higher-value tasks while AI does the busy work.
Maximize content value: How facial recognition search in DAM drives ROI
Bynder’s AI-powered digital asset management platform features facial recognition search to make it easy for organizations to manage vast digital asset libraries. Bynder’s Face Recognition feature helps boost content ROI by making it faster, easier, and smarter to locate, reuse, and manage digital assets across your enterprise. The result? A higher return on every dollar spent creating or licensing content.
Face Recognition is one of the many AI-powered DAM features to improve your workflow. Book a demo today to see how AI Search Experience offerings like Text-in-Image Search, Natural Language Search (NLS), Speech-to-Text, Duplicate Finder, Similarity Search, and Search by Image help make it easy to organize and discover assets.
Learn how to use Bynder’s Face Recognition feature, take a guided demo.
References
Klosowski, Thorin. Facial Recognition Is Everywhere. Here’s What We Can Do About It. New York Times. Jul. 15, 2020. https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/
