Seeing Double: Machine Vision, Difference, and Repetition



In thinking about human and machine vision in relation to doppelgangers, I turn to Francois Brunelle’s photographic project “I’m not a look-alike.” Here the artist captured people that were not related yet looked very similar. This uncanny resemblance was attributed to shared DNA. Brunelle’s lengthy quest for finding our doppelganger is part of a larger existential question about one’s uniqueness and thus individuality. A quest that is now seen as being attainable in a quick and efficient manner by algorithms that can compare millions of DNA codes. As an article from the New York Times from 2022 suggests, rendered through machine vision, a saliva swab now can reveal and verify not just our ancestry but also our real-world living doubles. In the popular imaginary, machine vision can also uncover based on DNA analysis our true soulmate. This is indeed the premise of Netflix’s original series The One. While these accounts are oriented towards a future, machine vision is already deployed in facial recognition surveillance practices in which it aims to identify and verify unique individuals that can be continuously monitored. In the context of surveillance, visual resemblance can have serious personal consequences. The look-alike is thus both desired and feared. Adam Harvey’s project MegaPixels takes on this dualistic function of the double by exposing the presence of doppelgangers on Flickr, where Flickr was used extensively for the establishment of training data sets. Taking on a Deleuzian framework of difference and repetition, this project explores perceptions of machine vision in the shaping media of verification in popular culture, artistic, as well as surveillance contexts.

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Keywords: algorithmic culture, verification, platform seeing, distributed verification, distributed identity

How to Cite: Hristova, S. (2023) “Seeing Double: Machine Vision, Difference, and Repetition”, communication +1. 10(1). doi: