Standard methods for biometric Authentication, think fingerprint scanning and facial recognition, are extremely problematic given the current cultural expectation that masks, and often gloves, be worn throughout the day. UnifyID has launched a new gait-based biometric authentication API that hopes to solve these problems.
The new UnifyID GaitAuth APITrack this API provides developers with a more passive version of biometric authentication. By analyzing the way that a user walks, a collection of attributes that are highly individualized, the API is able to authenticate a user in the background, without direct interaction on the users part. This allows for the device that the resulting application is running on to become a hardware key of sorts.
UnifyID makes all this happen by leaning on proprietary machine-learning algorithms that do all the work in the background. The company points to this as an advantage. One of the challenges of properly implementing biometric authentication is having users submit enough high-quality data for the engine to authenticate with a high level of certainty. By collecting this data in the background via a passive process, gait analysis is able to ensure sufficient data.
Developers can check out UnifyID’s documentation for the API and SDK.