The other key part of digital identity verification is to match the applicant's photo on an ID card or selfie image with the selfie video. The PresentID PhotoID matching (face matching) API/SDK evaluates whether two faces belong to the same person or not. Face verification ensures a one-to-one (1:1) match of a face image captured at the time of integration with an image captured from a trusted credential such as a driver's license or passport.

We have created a very deep convolutional neural network to extract very high-level features from a face for each person. We provide a large-scale image database of faces from many sources, e.g. web crowdsourcing, our in-house integrated dataset. The database contains a wide variety of breeds. The inference time of our model is 115ms on an intel corei7 6700k processor. Mostly in our solution, we save some images from client's SDK which are selected based on our own algorithm. This feature makes our pairing stronger. In addition, we record the checksum of a video to check for duplicate videos.

Our method has shown promising results with wide variations in Appearance, eg pose, age gaps, skin tone, glass, makeup and beard.