It's President Obama. So? This isn't about how well you are at recognizing popular faces in a digitized photograph - but congratulations anyway. Think of the last photo you were photographed in. Go ahead, check Facebook and see who tagged you - I'll wait.
Did you see yourself? Think, who or what was standing behind the camera? Not the photographer, I mean behind even them. Dr. Rob Jenkins of the University of York and Christie Kerr of the University of Glasgow published an article in the PLOS ONE journal stating that people can be identified by the reflections in an eye's retina in photography. People can be identified by their reflections in someone's retina. Let me say it for you: cool.
Here's the official abstract from the journal's website: "Criminal investigations often use photographic evidence to identify suspects. Here we combined robust face perception and high-resolution photography to mine face photographs for hidden information. By zooming in on high-resolution face photographs, we were able to recover images of unseen bystanders from reflections in the subjects' eyes. To establish whether these bystanders could be identified from the reflection images, we presented them as stimuli in a face matching task. Accuracy in the face matching task was well above chance (50%), despite the unpromising source of the stimuli. Participants who were unfamiliar with the bystanders' faces ... performed at 71% accuracy ... and participants who were familiar with the faces ... performed at 84% accuracy. In a test of spontaneous recognition, observers could reliably name a familiar face from an eye reflection image. For crimes in which the victims are photographed (e.g., hostage taking, child sex abuse), reflections in the eyes of the photographic subject could help to identify perpetrators."
To translate, people are markedly accurate at identifying people in small digitized images - familiar or no. While I'm unfamiliar with the sample, I'm willing to be these surveyed persons aren't "super recognizers" as Carole Moore describes in Law Enforcement Technology: January. These people have a self explanatory skill of the ability to recognize faces even if seen for a split second. She postulates crimes could be averted by these people watching real-time surveillance watching for, say, drug deals by identifying known dealers. Is this the law enforcement technology we've been promised in the science fiction novels? This is evidence of practical technologies to come years from now. Was the idea of a flying robot beam radar to locate persons in a building/rubble? (Check out the Xaver AID.) Robots have always been around, but with Google publicly showing interest makes it ... just that much for interesting ... just that much more viable perhaps?
The experiment used a 39 megapixel camera (it'll be common soon enough) and took "passport style photographs" while volunteers stood behind the camera as to be seen by the acting model. Reflected images IN THE EYES were smaller than a subject's face by a factor of 30,000. I didn't know what this meant until I saw this video (you should find it above).
The scientists explain how this concept could be useful perfectly: "...for crimes in which victims are photographed, corneal image analysis could be useful for identifying perpetrators. As with other sources of forensic evidence (e.g. fingerprints), corneal reflection images may not always be readily available. In particular, clear corneal reflections require the subject's face to be in focus, and viewed from a roughly frontal angle under good lighting. They also require high image resolution in order for bystanders' faces to be properly resolved. We note that pixel count per dollar for digital cameras has been doubling approximately every twelve months. This trajectory implies that mobile phones could soon carry >39 megapixel cameras routinely. However, as the current study emphasizes, the extracted face images need not be of high quality in order to be identifiable. For this reason, obtaining optimal viewers - those who are familiar with the faces concerned - may be more important than obtaining optimal images."