These neural networks are trained on thousands or even millions of examples of the types of problems the system is likely to encounter, allowing the model to “learn” how to correctly identify patterns from the data.
Today, facial recognition systems are powered by deep learning, a form of AI that operates by passing inputs through multiple stacked layers of simulated neurons in order to process information. However, as performance improvements create incentives for more widespread deployment, the need to assure proper governance of the technology will only become more pressing.įacial recognition systems are a sub-field of AI technology that can identify individuals from images and video based on an analysis of their facial features. Further accuracy gains will continue to reduce risks related to misidentification, and expand the benefits that can come from proper use. Government action should be calculated to address the risks that come from where the technology is going, not where it is currently. These improvements must be taken into account when considering the best way to regulate the technology.
As of April 2020, the best face identification algorithm has an error rate of just 0.08% compared to 4.1% for the leading algorithm in 2014, according to tests by the National Institute of Standards and Technology (NIST). As of 2018, NIST found that more than 30 algorithms had achieved accuracies surpassing the best performance achieved in 2014. How Accurate are Facial Recognition Systems – and Why Does It Matter?įacial recognition has improved dramatically in only a few years.
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