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Presidential Portraits As Digital Glitch Collages

Olivier Ratsi, from AntiVJ, deconstructs the images of president’s past.

In the run up to the presidential election a couple of week’s back, coverage of the race and the two nominees went to saturation point and beyond. Every conceivable, possible way of analyzing the two candidates, previous elections, and anything that had the word “president” in it had been ruminated over, pondered, chewed up, and spat out by the media—or at least it seemed that way. But they missed something, and they’re probably going to kick themselves when they hear the idea: creating digital glitch collage portraits of presidents from down the ages.

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In his series Once Upon a Time the Presidents, Olivier Ratsi from visual art collective AntiVJ has done just that, reconstructing portraits of presidents like you would a forensic facial reconstruction (but with added glitching). Like, taking the ear of Dubya and mixing it with the brow of Nixon to create some kind of uber crook.

Ratsi uses Photoshop to make the glitch portraits and explained the project to me below:

I essentially produce work that revolves around representations of perceptions of the world and the experience of reality. For example Anarchitecture is a project about space.

And I have adopted glitches in my work. The glitch process affects the image received by the observer. It creates a disturbing failure, and a quite consistent effect, so that the observer cannot fully decode the information. However this break acts—without preventing the observer from reconstructing an image—through his own life experience.

In Once Upon a Time the Presidents the project resonates with the current presidential election. I visually deconstruct the president, both past and present. Its aim is to generate a break with the meaning of the original items, to propose a new viewing angle and to provide the public a new field of experience, another way of looking at space and time.

You can find out more about Ratsi’s work, and that of his AntiVJ colleagues, in our short doc below.

@stewart23rd