Deep neural network facial recognition models like OpenFace, VGGFace are achieving amazing results on facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.
These models combined with the right training data, can be used by banks, telcos and other credit providers to verify the identity of their consumers. For example to compare a selfie image against the picture of their government issued ID. Adding OCR can allow further verification of personal information, such as their name, date of birth, ID number, etc.
We will compare various model architectures, embeddings, data augmentations, training datasets used for facial recognition applications.
Arshak Navruzyan is a machine learning focused product manager. He founded Fellowship.AI applied machine learning fellowship program and is the Chief Technology Officer at Sentient Technologies. Arshak has delivered AI solutions for multi-billion dollar quantitative hedge funds, numerous venture funded startups and some of the largest telecoms in the world. Arshak has been in technology leadership roles at Argyle Data, Alpine Data Labs, Endeca/Oracle.
Sharath Kalkur is a Masters graduate from University of Illinois at Chicago, majoring in Electrical and Computer Engineering. He specialized in the field of Machine Learning and Data Sciences, Computer Vision and Processor Technology. He has previously worked as a Machine Learning Intern and AI Architect at TrueMedicines Inc. and a Machine Vision Engineer at Waec, LLC. Sharath is now a Machine Learning Fellow at Fellowship.AI. He has worked closely on Launchpad.AI's Identity over the course of his fellowship.