FACE RECOGNITION WITH LOW FALSE POSITIVE ERROR RATE

Bagrov, N. U.; Konushin, A. S.; Konushin, V. S.

Nowadays face recognition systems are widely used in the world. In China these systems are used in safe cities projects in production, in Russia they are used mostly in closed-loop systems like factories, business centers with biometric access control or stadiums. Closed loop means that we need to identify people from a fixed dataset: in factory it’s a list of employees, in stadium it’s a list of ticket owners. The most challenging task is to identify people from some large city with an open dataset: we don’t have a fixed set of people in the city, it’s rapidly changing due to migration. Another limit is the accuracy of the system: we can’t make a lot of false positive errors (when a person is incorrectly recognized as another person) because number of human operators is limited and they are expensive. We propose an approach to maximize face recognition accuracy for a fixed false positive error rate using limited amount of hardware.

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Zitierform:

Bagrov, N. U. / Konushin, A. S. / Konushin, V. S.: FACE RECOGNITION WITH LOW FALSE POSITIVE ERROR RATE. 2019. Copernicus Publications.

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Rechteinhaber: N. U. Bagrov et al.

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