Reflection-in-the-eye

Extracting Images from Reflections in the Eye

Ever thought about whether you could zoom in on someone’s eye in a photo and analyse the reflection on it? Read on to find out what research has done in this respect!

A previous post of mine discussed the idea of enhancing regions in an image for better clarity much like we often see in Hollywood films. While researching for that post I stumbled upon an absolutely amazing academic publication from 2013.

The publication in question is entitled “Identifiable Images of Bystanders Extracted from Corneal Reflections” (R. Jenkins & C. Kerr, PloS one 8, no. 12, 2013). In the experiments that Jenkins & Kerr performed, passport-style photographs were taken of volunteers while a group of bystanders stood behind the camera watching. The volunteers’ eyes were then zoomed in on and the faces of the onlookers reflected in the eyes were extracted, as shown in the figure below:

Zooming-face
(Image adapted from the original publication)

Freaky stuff, right!? Despite the fact that these reflections comprised only 0.5% of the initial image size, you can quite clearly make out what is reflected in the eye. The experiments that were performed also showed that the bystanders were not only visible but identifiable. Unfortunately, with a small population size for the experiments, this technically makes the results statistically insignificant (the impact factor of the journal in 2016 was 2.8, which speaks for itself) – but who cares?! The coolness factor of what they did is through the roof! Just take a look at a row of faces that they managed to extract from a few reflections captured by the cameras. Remember, these are reflections located on eyeballs:

reflections-eye-row-faces
(Image taken from the original publication)

With respect to interesting uses for this research the authors state the following:

our findings suggest a novel application of high-resolution photography: for crimes in which victims are photographed, corneal image analysis could be useful for identifying perpetrators.

Imagine a hostage taking a photo of their victim and then being recognised from the reflection in the victim’s eye!

But it gets better. When discussing future work, they mention that 3D reconstruction of the reflected scene could be possible if stereo images are combined from reflections from both eyes. This is technically possible (we’re venturing into work I did for my PhD) but you would need much higher resolution and detailed data of the outer shape of a person’s eye because, believe it or not, we each have a differently shaped eyeball.

Is there a catch? Yes, unfortunately so. I’ve purposely left this part to the very end because most people don’t read this far down a page and I didn’t want to spoil the fun for anyone 🙂 But the catch is this: the Hasselblad H2D camera used in this research produces images at super-high resolution: 5,412 x 7,216 pixels. That’s a whopping 39 megapixels! In comparison, the iPhone X camera takes pictures at 12 megapixels. And the Hasselblad camera is ridiculously expensive at US$25,000 for a single unit. However, as the authors state, the “pixel count per dollar for digital cameras has been doubling approximately every twelve months”, which means that sooner or later, if this trend continues, we will be sporting such 39 megapixel cameras on our standard phones. Nice!

Summary

Jenkins and Kerr showed in 2013 that extracting reflections on eyeballs from photographs is not only possible but faces on these reflections can be identifiable. This can prove useful in the future for police trying to capture kidnappers or child sex abusers who frequently take photos of their victims. The only caveat is that for this to work, images need to be of super-high resolution. But considering how our phone cameras are improving at a regular rate, we may not be too far away from the ubiquitousness of such technology. To conclude, Jenkins and Kerr get the Noble Peace Prize for Awesomeness from me for 2013 – hands down winners.

 

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Enhancing image meme hollywood

Is image enhancing possible? Yes, in a way…

I was rewatching “Bourne Identity” the other day. Love that flick! Heck, the scene at the end is one of my favourites. Jason Bourne grabs a dead guy, jumps off the top floor landing, and while falling shoots a guy in the middle of the forehead. He then breaks his fall on the dead body he took down with him. That has to be one of the best scenes of all time in the action genre.

But there’s one scene in the film that always causes me to throw up a little in my mouth. It’s the old “Just enhance it!” scene (minute 31 of the movie) and something we see so often in cinemas: people scanning security footage and zooming in on a face. When the image becomes blurry they request for the blur to dissipate. The IT guy waves his wand and presto!, we see a full resolution image on the screen. No one stands a chance against magic like that.

But why is enhancing images as shown in movies so ridiculous? Because you are requesting the computer to create new information for the extra pixels that you are generating. Let’s say you zoom in on a 4×4 region of pixels and want to perform facial recognition on it. You then request for this region to enhance. This means you are requesting more resolution, say 640×480. How on earth is the computer supposed to infer what the additional 307,184 pixels are to contain?

Enhancing image example

The other side to the story

However!!! Something happened at work that made me realise that the common “Enhance” scenario may not be as far-fetched as one would initially think. A client came to us a few weeks ago requesting that we perform some detailed video analytics of their security footage. They had terabytes of the stuff – but, as is so often the case, the sample video provided to us wasn’t of the best quality. So, we wrote back to the client stating the dilemma and requested that they send us better quality footage. We haven’t heard back from them yet, but you know what? It’s well possible that they will provide us with what we need!

You see, they compressed the video footage in order for it to be sent over the Internet quickly. And here is where the weak link surfaces: transferring of data. If they could have sent the full uncompressed video easily, they would have.

Quality vs transmission restraints

So, back to Hollywood. Let’s say your security footage is recording at some mega resolution. NASA has released images from its Hubble Space Telescope at resolutions of up to 18,000 x 18,000. That’s astronomical!! (apologies for the pun). At that resolution, each image is a whopping 400MB (rounded up) in size. This, however, means that you can keep zooming in on their images until the cows come home. Try it out! It’s amazing.

But let’s assume the CIA, those bad guys chasing Bourne, have similar means at their disposal (I mean, who knows what those people are capable of, right!?). Now, let’s say their cameras have a frame rate of 30 frames/sec, which is relatively poor for the CIA. That means that for each second of video you need 12GB of storage space. A full day of recording would require you to have 1 petabyte of space. And that’s just footage from one camera!

It’s possible to store video footage of that size – Google cloud storage capacities are through the roof. But, the bottleneck is the transferring of such data. Imagine if half a building was trying to trawl through security footage in its original form from across the other side of the globe.

The possible scenario

See where I’m going with this? Here is a possible scenario: initially, security footage is sent across the network in compressed form. People scan this footage and then when they see something interesting, they zoom in and request the higher resolution form of the zoomed in region. The IT guy presses a few keys, waits a 3 seconds, and the image on the screen is refreshed with NASA quality resolution.

Boom! 

Of course, additional infrastructure would be necessary to deal with various video resolutions but that is no biggie. In fact, we see this idea being utilised in a product all of use on a daily basis: Google Maps. Each time you zoom in, the image is blurry and you need to wait for more pixels to be downloaded. But initially, low resolution images are transferred to your device to save on bandwidth.

So, is that what’s been happening all these years? No way 🙂 Hollywood isn’t that smart. The CIA might be, though. (If not, and they’re reading this: Yes, I will consider being hired by you – get your people to contact my people).

Summary

The old “enhance image” scene from movies may be annoying as hell. But it may not be as far-fetched as things may initially seem. Compressed forms of videos could be sent initially to save on bandwidth. Then, when more resolution is needed, a request can be sent for better quality.

 

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