Standard pet ID tools, like tags and chips, are imperfect. Tags become easily detached, and not every owner is comfortable with the idea of microchipping their pet. Even those who are comfortable often run into problems with microchips, like chip damage and outdated ID databases.
The challenge inspired Jesse Joonho Lim and Ken Daehyun Pak to launch an app, Petnow, that they claim can identify cats and dogs by scanning their faces. Petnow, which has raised $5.25 million in funding so far from Daedeok Venture Partners and DigiCap at a $24 million valuation, is a participant in the Startup Battlefield 200 at TC Disrupt 2023.
Before founding Petnow in 2018, Lim co-lead a semiconductor startup called Chips&Media, which later pursued an IPO. Pak — who, like Lim, has a doctorate in electrical engineering — had been working as an AI video processing researcher for over a decade before joining Petnow.
At a high level, Petnow works by running a camera-based scan of a pet’s face from a mobile app for Android and iOS. Leveraging AI trained on a set of around 200,000 images of dog and cat snouts, collected both by the Petnow team and sourced from users’ pets, Petnow creates a biometric profile of a pet that’s unique to them.
Lest you be concerned the app accidentally captures, say, a family member standing behind a pet, Petnow claims to use an algorithm to automatically detect and hone in on dogs or cats while cropping out the rest.
For dogs, Petnow records a “nose print.” Yes — a nose print. The startup claims that a dog’s nose is as unique as a human fingerprint and doesn’t change over time, making it a reliable way to distinguish between puppers. For cats, Petnow looks at a cat’s “facial contour,” which Petnow says remains distinctive due to cats’ individual “grooming habits.” (To this reporter, that sounds a little more dubious than a dog’s nose print — but I digress.)
Lim and Pak envision people using Petnow to register their pets without visiting a vet, find missing pets and create “pet IDs” for checking insurance status.
“The pet identification market may not be mature at the moment, but it will eventually become big,” Lim and Pak told TechCrunch in an email interview. “Pet identification technology is a fundamental product that can be continuously used by people, unlike products or services that go viral only for short periods of time. The market has great potential, because pets should have IDs like people, and their data can be backed up to form an ultimate pet platform.”
But the question is, does the tech work as advertised?
Petnow claims its algorithms are “99% accurate” at identifying individual cats and dogs. It’s well understood, however, that even the best image-analyzing AI is prone to bias — intentional or no.
For example, at least six people, all Black, have been mistakenly arrested by police using facial recognition technology. Facial recognition algorithms are often trained on datasets that lack a critical mass of Black faces — introducing biases. Or, they’re trained on mugshot databases that contain an overwhelming number of Black faces, many shot in poor and grainy lighting conditions that interfere with the algorithm’s ability to distinguish one face from another.
Setting aside the challenges unique to facial recognition for a moment, in the animal realm, even experts struggle to tell the difference between breeds — let alone animals of the same breed. A recent study involving 5,000 dog experts nationwide found that only a small minority could pick out even one of the breeds identified in the dogs by their DNA.
Petnow claims that its training database is continually growing and that it uses AI to ensure pet photos are taken with the best possible brightness and sharpness. (In the same breath — perhaps anticipating questions about data privacy — Petnow says that it doesn’t provide a user’s or pet’s information to third parties without the user’s consent and offers an option to delete stored data at any time.) And Petnow points to a study co-authored by its data scientists in the journal IEEE Access, which shows that its dog nose-print identifying tech was over 99% accurate at distinguishing between noses.
The study dates back to 2021, though, when the training dataset was presumably smaller. And while Petnow claims it’s working on a companion paper for its cat face-recognizing algorithm, it’s yet to make that research public.
The stakes are high. One can imagine an algorithmic mistake stymieing a family’s search for a missing pet, or causing a vet to pull up the wrong animal’s vaccination records.
Those aren’t imminent threats, to be fair, given Petnow’s relatively slow uptake among pet care providers and shelters. While Petnow has around 70,000 users at present, it’s only signed five undisclosed enterprise and public sector customers in France, Toronto and South Korea (where the company is based).
Petnow is pre-revenue, with a $150,000-per-month burn rate. But it anticipates a contract with Korean domestic and international pet insurers by October and pilots in France and a metropolitan government in Canada for their pet registries.
“Thanks to the pandemic, the pet population has grown more rapidly and people spend more time with their pets … There’s still huge room to grow,” Lim and Pak said. “Government pet registry and affiliation programs with pet insurance providers are currently on the way from South Korea, and we’ll have our product enterprise-ready for the North America and Europe regions soon.”
I only hope that Petnow — and its rivals — deliver on their promises of accurate pet identification. To fall short would be irresponsible; misleading pet owners feels like an exceptionally cruel form of deceptive advertising.