The success of VCSEL technology has led to their use in a wide range of sensing, imaging, scanning and ranging applications across multiple fields - machine vision, automotive, scientific and medical.
Facial recognition is a category of biometric software that maps an individual’s facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual’s identity.
High-quality cameras in mobile devices have made facial recognition a viable option for authentication as well as identification. Apple’s iPhone X, for example, included Face ID technology for the first time, allowing users unlock their phones with a faceprint mapped by the phone’s camera. The imaging technique, which is designed to resist being spoofed by photos or masks, captures and compares a pattern of over 30,000 variables and is generally referred to as ‘structured light’. Currently Face ID can be used to authenticate purchases with Apple Pay and in the iTunes Store, App Store and iBooks Store. Apple encrypts and stores faceprint data in the cloud while authentication takes place directly on the device.
Uses of Facial Recognition Technology
- A research team at Carnegie Mellon has developed a proof-of-concept iPhone app that can take a picture of an individual and within seconds return the individual’s name, date of birth and social security number.
- The Google Arts & Culture app uses facial recognition to identify museum doppelgangers by matching a real person’s faceprint with portrait’s faceprint.
- Professor Shen Hao of the Communications University of China uses facial recognition technology to track students’ attendance.
- Amazon, MasterCard and Alibaba have rolled out facial recognition payment methods commonly referred to as selfie pay.
Structured Light 3D Camera
- Interpolating pattern ‘distortions’ reflected from surfaces, determines depth by comparing patterns via triangulation
- Single-shot (pseudo-random dot pattern for example) provides good resolution, accuracy and response speed
- Multi-shot systems uses a series of images, varied by time and/or spatial encoding to increase precision and accuracy
- Requires more complex hardware, software and processing and results in slower speed