Unlocking the potential of facial recognition technology in healthcare

Technology in healthcare is not just transforming and improving the quality of patient treatment tremendously, but also reducing medical errors. The use of biometrics, patient portals, telehealth and telemedicine, wellness and fitness apps, blockchain and electronic health record (EHR) systems have significantly enabled proactive healthcare and patient engagement. Biometric verification uses unique identifiers like fingerprints, face, hand geometry, earlobe geometry, retina and iris patterns, voice waves, DNA, and signatures. Hospitals are leveraging this technology for identifying and authenticating patients.

Of all the biometric technologies, facial recognition is contactless and, hence, the least invasive technology. It has a short processing time and a high recognition rate and functions despite the angle and facial changes. A facial scanner reads the geometry of your face by measuring the distance between the eyes, depth of the eye sockets, width of the nose, shape of the cheekbones, and the length of the jaw line. It then traces your features on a grid as values which are then transferred and saved to a database as an algorithm. This makes it easy to access a person’s facial identity and verify it, giving optimal results through adaptive regional blend matching (ARBM) technology.

Facial recognition has greatly empowered the healthcare system by facilitating a seamless digital experience for both patients and caretakers. It ensures the security and privacy of patients by transmitting and storing information regarding identities and health. It not just helps with patient identification and tracking, but also pharmacy dispensing, care provider authentication, home / remote patient access; while reducing identity thefts. Facial recognition systems can secure certain rooms in the hospital like intensive care unit (ICU) and operation theatre to keep unauthorized people from entering. It does not require patients to carry any identity card or remember any information regarding their identities like usernames and passwords, and it is ideal for identifying patients with certain mental illnesses and developmental or genetic disorders like Down syndrome who cannot talk.

There are various methods of facial recognition like generalized matching face detection method (GMFD), perturbation space method (PSM), adaptive regional blend matching method (ARBM), and more that use different algorithms. The GMFD method uses a modified generalized learning vector quantization (GLVQ) algorithm based on a neural network. The GVLQ algorithm searches and matches people after verifying their eyes accurately even if they are wearing sunglasses or hats to hide their identity.

Having said that, facial recognition is only one component of the object recognition universe. Through object recognition technology, businesses can automate data extraction from both textual and non-textual sources. A case in point is AntWork’s ANTstein. By using ANTstein, businesses can retrieve data from various sources and process it using robotic process automation and analytics to achieve valuable insights. What’s more, it is the only robotic process automation (RPA) platform in the market to have all four building blocks for supporting an automation and digital transformation journey. To know more about ANTstein, e-mail us at hello@ant.works


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