Facebook’s new artificial intelligence group recently published a research paper titled “DeepFace: Closing the Gap to Human-Level Performance in Face Verification” which describes its advances in facial recognition technology. The abstract is pretty technical so I highlighted the big takeaway that may interest you:
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities, where each identity has an average of over a thousand samples. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.25% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 25%, closely approaching human-level performance.
According to the MIT Technology Review’s article titled “Facebook Creates Software That Matches Faces Almost as Well as You Do”, human beings recognise faces correctly 97.53% of the time which makes DeepFace just about as accurate as humans when it comes to identifying your face. What does this mean for brands? Quite a lot although probably not right away.
One of the service features that will continue to distinguish brands and their service offerings is a brand’s ability to present its customers with a deeply personal and meaningful service. Brands have been working on ways to personalise their services for quite some time and have used demographics, location, culture and, more recently (and as we have increasingly seen on Facebook and Google properties), your interests. All of this information is being associated with your identity so when you connect to a site or an app with your Facebook profile, for example, you share your interests, connections and other signals from your profile with the site or the app which then customises your experience, tells you which of your friends are also using the site or the app (making it more likely that you will continue to use it) or do a number of other things to present a version of the site or the app that is more relevant to you.
Introducing accurate facial recognition into the mix potentially removes the need for you to tell Facebook (or a future Facebook connected site or app) who you are before your data is shared and your experience modified. All you will need to do now is show up and let a camera see you long enough to capture a reasonably clear image of your face. From there you will be identified, placed into a particular context and things will happen. As a brand, there are some interesting opportunities. Imagine your guests arrive at your event and, instead of relying on guests to manually check in, a webcam at the door connected to your Facebook Page recognises the guests as they arrive and posts an update in your stream sharing their arrival. This isn’t happening yet but it is very possible.
Even though this technology is not implemented particularly widely, accurate facial recognition associated with identities and personal information profiles is probably not far off. It is going to scare consumers who will become aware of the myriad cameras and opportunities for them to be identified and located in specific contexts. The remnants of their privacy (by obscurity) will be whittled down to almost nothing and they won’t expect it. As a brand, this technology offers a number of opportunities to engage with customers in a very meaningful and personal way but catching them by surprise is almost certainly going to backfire, largely because the backlash will be so much more intense, precisely because the possible applications of this technology are so personal.
Preparing customers for implementations of these sorts of technologies and reducing the risk of significant reputational harm requires transparency and a healthy dose of courage to be as transparent as you need to be about how you intend engaging with your customers. As I pointed out in my talk at the recent SA Privacy Management Summit, brands have little to gain by being opaque. Transparency is a critical risk management tool, it engenders trust and keeps brands accountable and honest. That is scary for brands not accustomed to being in the spotlight but if they want to engage more effectively with their customers and earn their loyalty, they can’t do it by being evasive and catching their customers by surprise.
Widespread facial recognition will have a fairly profound impact on data protection when businesses adopt it on a larger scale. The opportunities for brands are tremendous and could, literally, revolutionise how a customer perceives a brand. To paraphrase a worn adage, with this great power comes great responsibility and brands should think carefully about how to introduce these tools to their customers and obtain their buy-in. Even though facial recognition is still in fairly limited use, brands have been using various tools and techniques to leverage customers’ identifies and personal data to customise their experiences of a brand’s products and services for some time now. Transparency is more likely to win customers’ trust even though it scares many brands silly. That said –