Open AccessBenchmarking human face similarity using identical twins
Author(s) -
Sami Shoaib Meraj,
McCauley John,
Soleymani Sobhan,
Nasrabadi Nasser,
Dawson Jeremy
Publication year - 2022
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/bme2.12090
Subject(s) - benchmarking , computer science , similarity (geometry) , face (sociological concept) , artificial intelligence , structural similarity , information retrieval , pattern recognition (psychology) , data science , natural language processing , image (mathematics) , social science , marketing , sociology , business
Abstract The problem of distinguishing identical twins and non‐twin look‐alikes in automated facial recognition (FR) applications has become increasingly important with the widespread adoption of facial biometrics. Due to the high facial similarity of both identical twins and look‐alikes, these face pairs represent the hardest cases presented to facial recognition tools. This work presents an application of one of the largest twin data sets compiled to date to address two FR challenges: (1) determining a baseline measure of facial similarity between identical twins and (2) applying this similarity measure to determine the impact of doppelgangers, or look‐alikes, on FR performance for large face data sets. The facial similarity measure is determined via a deep convolutional neural network. This network is trained on a tailored verification task designed to encourage the network to group together highly similar face pairs in the embedding space and achieves a test AUC of 0.9799. The proposed network provides a quantitative similarity score for any two given faces and has been applied to large‐scale face data sets to identify similar face pairs. An additional analysis that correlates the comparison score returned by a facial recognition tool and the similarity score returned by the proposed network has also been performed.