Significant advances have been made in the health category. The main application categories where facial recognition is being used, are for security and law enforcement, health, banking, and retail.
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Public sector surveillance and numerous other applications in diverse market segments are the two most important drivers of this development.Īccording to the study, the top facial recognition vendors include Accenture, Aware, BioID, Certibio, Fujitsu, Fulcrum Biometrics, Thales, HYPR, Idemia, Leidos, M2SYS, NEC, Nuance, Phonexia, and Smilepass. There is no physical interaction required by the end-user.Īdditionally, A study published in June 2019, estimates that by 2024, the global facial recognition market would generate $7billion of revenue, supported by a compound annual growth rate (CAGR) of 16% over the period 2019-2024. That’s because it’s easy to deploy and implement. Finally, the system tries to recognise the face and match it to a name stored in the database.įacial biometrics continues to be the preferred biometric standard used. The process of facial recognition is usually defined as a five-step process:įeature extraction involves obtaining relevant facial information to determine whether the object is human.
How Facial Recognition Technology Worksīy providing an image or video of an individual, facial recognition software identifies and authenticates them through using a set of recognisable and verifiable data unique and specific to that person. Two key areas where machine learning has grown exponentially are the inclusion of facial and voice recognition in a mobile application.
While several machine learning algorithms have been around for a long time, a recent breakthrough is the ability to apply complex mathematical equations to big data automatically. Data, and lots of it, is the key to making machine learning work. Machine learning is driving an explosion in the capabilities of artificial intelligence, from offering security solutions to monitoring patient medication – helping software make sense of the messy and unpredictable real world. Machine learning helps computers to solve tasks that have, until now, only been performed by humans.
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