Vintra Releases Results On Reducing Racial Bias In Facial Recognition


Vintra, a maker of video analytics powered by machine learning and artificial intelligence (AI), is working to end racial and ethnic bias in AI-based facial recognition has released the results from a year-long effort to ensure Vintra’s AI platform can equitably recognize and correctly identify faces across different races. Vintra’s work to reduce bias has resulted in cutting the bias gap by more than two-thirds and surpassing the accuracy rates across most racial and ethnic identities of the leading commercially available face recognition algorithms from Microsoft and Amazon, and those of the leading open-source face recognition algorithms like ArcFace.

Vintra has built and curated their own data set, pulling from over 76 countries and tens of thousands of identities with dozens of reference images for each identity in order to better represent Caucasian, African, Asian and Indian ethnicities. This work has resulted in a much fairer balance with each group representing roughly 25% of the total data population and giving AI-powered video analytics a truer picture of what our world really looks like. Ensuring the accuracy of Vintra’s facial recognition results remained in the top 10% of solutions globally when tested on leading datasets like RFW, the team set out to reduce the bias gap – the percentage delta between correctly identifying white faces and all other non-white identities.

Today, publicly available academic algorithms have an 8% difference in bias between black and white faces, while commercially available algorithms have a 9% difference. Some companies, notably Microsoft and Amazon have a 12 percentage point difference when looking at white and black faces.

With initial testing of the new dataset and algorithm, Vintra has closed the racial bias gap to 4.7 when comparing Caucasian and African-descent faces. Vintra’s average accuracy across all non-Caucasian categories beats popular APIs from Amazon, Microsoft, and the leading open-sourced algorithms on the market.

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