Face recognition and cyberattack analysis can be accelerated by up to 20 times using NEC neuroscience-inspired AI technology
NEC today announced the development of artificial intelligence (AI) technology that makes high-speed decisions while maintaining high accuracy in real-time analysis of time series data. The technology is based on brain activities during complex decisions which require sequential evidence accumulation. The Sequential Probability Ratio Test (A method of extracting data one by one and making decisions at the same time, stopping data collection at the timing when it is recognized that a conclusion has been reached) that this technology is based on was first proposed in the 1940s and adopted for quality-control in the manufacturing field.
Typical AI engines for face recognition and cyberattacks depend on a preset amount of data to be collected before making a decision. For example, at entrance gates that utilize face recognition, individuals are authenticated by taking a previously fixed number of frames in succession, followed by a final decision. NEC’s new technology collects and analyzes data without a previously fixed amount of data required. Inspired by neuroscience, the technology makes a decision as quickly and accurately as possible by accumulating evidence until a certain confidence level (likelihood) is reached. Since additional data collection is unnecessary after reaching the desired confidence level, computations can be accelerated when compared to conventional approaches.
NEC is applying this technology to the facial recognition AI-engine NeoFace, which is at the core of NEC’s portfolio of biometric identification technologies, Bio-Idiom featuring the world’s No. 1 authentication accuracy. In addition, NEC will consider applying this technology to broader areas that utilize time series data, including detecting and analyzing cyberattacks and other unauthorized communications. This technology is expected to enable face recognition as well as cyberattack detection and analysis to be accelerated by up to 20 times while maintaining the same accuracy as existing methods.