Reengineering an SDK with ML-powered features for a global biometrics technology company

Daffodil was quick to innovate on ideas to implement the SDK’s enhancements in a cost-effective manner, while providing short turnarounds and an uninterrupted development lifecycle. The utilization of cutting edge Machine Learning platforms and dependencies such as ML Kit and TensorFlow ensured that the final solution was state-of-the-art. The enhancements carried out as per the requirements were as follows:

Precise Versioning

The Daffodil, at the outset, demarcated their efforts into the development of a trial version of the SDK for online stores, a basic showcase version and a subscription-based definitive version. The showcase version would be used to demonstrate the SDK’s features to potential clients who would go on to cement the deal to use the full version. The trial version is available on the Play Store and Apple Store for anyone to check and goes by IDentity 2.0.

Image Calibrations

Due to the use of Camera X, a Jetpack library, in the development of the SDK, the biometric’s provider’s clients would not have to worry about device-specific nuances that their end-users may have to deal with. A buffer seamlessly passes the images captured over to ML Kit. The SDK also leverages Camera X to prompt the user to use a better camera resolution in case the existing one doesn’t do a good enough job. 

Additionally, the aspect ratio, orientation, and rotation are kept consistent with its help. While capturing a customer’s photo, if the camera detects obstructions such as hats or glasses, the customer is prompted to remove the same.

ML-based Document Type Detection

If the document to be uploaded is not framed properly, there are prompts that tell the user to move the document captured a certain way only to include the necessary information for processing. Using ML Kit, the SDK has been trained with several images of different types of official documents used for KYC so that it recognizes which type of document is being uploaded, e.g., passport or social security card. Image-to-text capabilities can then pick up the relevant textual biometric data.

Frictionless SDK Integration

Clients signing up with the biometrics technologies provider to use the SDK can implement it into their application with just a single line of code instead of having to take care of third party dependencies and other operating system-based or environment configurations. Moreover, the associated documentation for SDK implementation is extremely comprehensive and easy to understand.