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When comparing to a database of stored mathematical models of known faces, an algorithm determines the best match and percentage of probability of accuracy is provided. The program’s increased ability is shown in additional related information available, like skin tone, color and style of clothing, backpack detection, vehicle type, and direction traveling.
For years, face recognition has enabled the capture of wanted criminals. Face analysis is also a crucial support for theft prevention and safety in transportation and in retail.
As facial recognition increases in efficacy, so does its number of commercial applications. There is particular growth in healthcare, IT, banking and marketing. Face scanners are increasingly used to improve customer experience and increase sales, by identifying celebrities and favored customers. Thanks to deep learning-based algorithms, face analysis is able to provide identity verification with a high degree of accuracy, even outperforming human facial recognition capabilities. In well-lit situations they can now detect mood. While misclassifications happen and sunglasses and hoods continue to reduce accuracy, the facial recognition segment is expected to double in growth by 2024.
Face Recognition links with many programs, including Intrusion Detection, License Plate Recognition and GPS, Access Control, Crowd Detection, and City Surveillance. The technology is expanding from 2D to 3D which is of particular value in identifying criminals and terrorists, in patrolling highways and in aiding forensics.
Предоставлено IronYun, Inc.
Airports,Critical Infrastructure,Education,Healthcare,Retail,Traffic Monitoring
Artificial intelligence,Traffic management,Video verification,Traffic Monitoring
Transport,Traffic Monitoring,Artificial Intelligence,LPR/ANPR,Traffic Management,System Integration