Aadhaar Facial authentication

The Unique Identification Authority of India (UIDAI) is all set to make face authentication available alongside iris or fingerprint scan as means of verifying Aadhaar users from July 1, 2018. The UIDAI, which is in-charge of the 12-digit identifier Aadhaar, in January had announced that it will introduce face authentication feature to help those who run into problems in biometric authentication due to old age, hardwork or worn-out fingerprints. It had said that face authentication will be allowed only in fusion mode meaning it would be permitted along with either fingerprint or iris or OTP to verify the details of Aadhaar holders.

It may be recalled that the Supreme Court is hearing arguments for and against the constitutional validity of the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act 2016. In a presentation to the court, UIDAI CEO Ajay Bhushan Pandey said 17 billion Aadhaar authentications and 4.6 Bn eKYC transactions have been performed so far. According to Wikipedia, a facial recognition system is a computer application capable of identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a face database. 

It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.Recently, it has also become popular as a commercial identification and marketing tool. Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face. 

For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features.Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data.[5] One of the earliest successful systems[6] is based on template matching techniques applied to a set of salient facial features, providing a sort of compressed face representation.