Croatian-based photographer Ino Zeljak blends images of related and un-related look-a-likes together to create the portraits from his "Metamorfoza" series. Dubbed the "Frankenstein of photography" by Complex Magazine, Ino finds the similarities between parents and their children, siblings of different genders and even best friends whose portraits are blended to create a brand new individual.
These blended portraits reveal mirrored facial structures, eye shapes and hairlines that appear seamlessly connected. The final result of the images is almost eerie and illustrates both the immense "similarities and differences between people that are connected genetically or with relationship."
Going beyond a traditional half-and-half split, Ino Zeljak blends his individual images smoothly and makes differentiating his characters a difficult feat for the viewer.
Superimposed Doppelganger Photography
Ino Zeljak's Photos Capture Related and Unrelated Subjects
Trend Themes
1. Blended Portraits - Creating portraits by seamlessly merging images of related and unrelated subjects presents an opportunity for unique and thought-provoking art.
2. Genetic Connections - Exploring the visual similarities between family members and the impact of genetics on physical appearance can lead to innovative approaches in fields like genetics research and ancestry testing.
3. Facial Structure Detection - Developing technologies or algorithms that can accurately analyze and blend facial features could revolutionize fields like facial recognition software and virtual makeup applications.
Industry Implications
1. Art - The art industry can incorporate blended portraits as a disruptive innovation to challenge traditional notions of portraiture and explore new artistic possibilities.
2. Genetics Research - Blended portraits can provide insights into genetic connections and hereditary traits, presenting opportunities for advancements in genetics research and understanding human variation.
3. Facial Recognition Technology - The field of facial recognition technology can benefit from advancements in facial feature detection and blending techniques, enhancing the accuracy and capabilities of facial recognition systems.