Going through a number of photos saved on my iPhone, Klik did a decent job at picking different friends. It did best with close-up shots, as long as there were no more than a few people in the frame, and seemed to have trouble with shots where people’s eyes were closed or their faces were obscured by sunglasses or hats. It also had trouble when people were far away or not looking right into the camera.
When you focus the Klik viewfinder on a person (or several people), the name of the person Klik thinks it’s seeing quickly pops up on the screen near that person’s head. But if the name is incorrect—perhaps because you’re not Facebook friends with them, or because you are but that person doesn’t have a lot of photos of themselves on Facebook—you can hit the “learn” button to teach Klik who it is. Klik will ask you to center the person’s head on the screen and then try again to determine who it is. If it still doesn’t make a match, you can search for the right person in your address book or type in their name to tag a photo. The next time you take a photo of that person with Klik, it should be better able to guess their name.
Klik sometimes makes understandable mistakes. It can identify me correctly with 95 percent certainty, but it also thinks there’s a chance I could be my mom, who does look like me, or an old friend from New York to whom I bear a decent resemblance.
Alessandro Acquisti, an associate professor at Carnegie Mellon University who has studied facial-recognition software, is concerned about possible privacy issues that could crop up over time if apps like Klik become more widely used and accepted. What happens, he wonders, if a Klik user’s Facebook friend doesn’t want to be recognized by the app? (As it turns out, Facebook’s privacy settings allow you to turn off your friends’ ability to share your photos with outside apps that are pulling data from Facebook.)
Acquisti does think that allowing users to correct errors is very powerful, though, and could dramatically decrease false identifications. “They’re effectively enlisting the users to improve their own algorithms,” he says.