This model can identify whether two images given as input are of the same person or not. The model uses a Deep Neural Network (DNN), which was trained end-to-end on large-scale face recognition datasets covering all ethnicities and genders.
This model was evaluated against four datasets: LFW, agedb, cfp-fp, and cfp-ff.
It achieves a true positive rate of 99.8% on LFW, 98.58% on agedb, 98.98% on cfp-fp, and 99.85% on cfp-ff.
This model determines whether two images contain the same person or not.
This model was trained on a dataset unique to Trueface that was compiled in-house from public and proprietary sources specifically to limit and combat biases present in publically available datasets alone.
This model ranks in the Top 10 for genuine template match on NIST 1:1 and was also tested on the Megaface, LFW, agedb, cfp-fp, and cfp-ff datasets and achieved state of the art performance.
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