The Challenge of Twins
Identical (monozygotic) twins present the hardest possible test for face recognition systems, two individuals with nearly identical genetic instructions for facial development, photographed in the same conditions. For decades, twin pairs were used as near-failure cases to establish the limits of face recognition performance.
Modern deep learning systems handle most twin pairs with reasonable accuracy under controlled conditions. The key word is controlled: the genuine biological differences between identical twins are small, and poor photo conditions (especially backlighting, extreme angles, or low resolution) can easily produce false matches between twins even for sophisticated systems.
Where the Differences Come From
Despite identical genomes, twins develop measurably different faces due to epigenetic variation and developmental noise. Position in the womb affects the mechanical forces on the developing skull, producing subtle differences in bone configuration. Asymmetric placental blood flow affects facial development. Random developmental noise produces microscopic differences in mole placement, skin feature distribution, and exact feature positioning.
Over time, these differences accumulate: different sun exposure, different muscle use patterns, and different life experiences leave different traces on the face. Twins who are difficult to distinguish in childhood often become easier to distinguish in middle age as these accumulated differences become more pronounced.