Portraits for Umwelts and Programs!

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How can humans sense what a computer program deems to be an image of a face? To
explore this question, this work involves photographs and drawings produced using facialrecognition programs included in digital cameras. Digital cameras of recent years
incorporate a facial recognition function. It is a function that displays a square on the monitor
around a face that is detected within the field of view. Such programs are generally based on
databases that aggregate enormous amounts of photographs of people’s faces. The
program first divides the subject area into successive rectangles, which are further
subdivided into smaller rectangles. It then measures the local rate of change of luminanceamong the rectangular areas. When the rate of change matches the one extracted from a
database of face photographs, the program determines that it is indeed a face. The
verification is carried out by countless “weak” detectors (referencing a database to identify
only certain patterns of change in luminance levels as faces / composing an assemblage of
detectors with different sets of criteria), which are used in order of their rates of successfulrecognition to detect a positive instance within view (Viola & Jones, 2001). The mechanismof such programs could perhaps be understood by reading papers, but difficulties
immediately emerge when one beings to think about how faces appear to programs. Whatkind of images are recognized as faces by the programs? To answer this, we must create“images that are recognized as faces exclusively by programs” that pose a counterpoint tohuman facial recognition. !

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This work juxtaposes photographs and drawings. In the photographs, the camera of an
iPhone 6 was used to make weird faces to seek out the kind of faces that are not recognizedas such by the program. Cameras should not recognize these photographs as faces. On theother hand, the drawings portray face-like images through an autopoietic generation ofpatterns and constituents achieved via improvised motifs (brush strokes and application ofpaint, colors and tones, etc.) that are repeated twice in the horizontal direction. 63 drawings
were produced in total, with 126 variations if we were to count the upside down positioningtoo. Each of those variations were then tested twice with the iPhone 6 camera to determine
whether faces were recognized, and were categorized into positives and negatives (seediagram). The drawings that are exhibited on wooden panels, along the photographs, arethose that were recognized as faces in their reverse position (only 4 out of 63 wererecognized as faces in both directions). In other words, these drawings represent the factthat the program is recognizing as faces those upside down images that do not appear
anything like faces to us. (Do your cameras behave in the same way?) !

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The weird faces that look like faces to humans but not to computers, and on the other hand,
upside down drawings that look like faces to computers but not to humans. Contraposing
these face recognition phenomena allows for the emergence of a highly abstracted facial
image supported by mathematical programs. Seeing those images allows us to experience
anew the human way of looking at faces. This work is thus a portrait that exists between
humans and computer programs. Will computers one day arrive at the kind of metacognitionof faces that this work presents? !