Sparse Bayesian reconstruction for optimal facial recognition

Near 100% accuracy is a necessary condition for facial recognition to become more widely utilised in security and to be more widely accepted by users. Existing techniques in the field of computer vision have taken us to unprecedented levels of accuracy. This project aims to close the final gap in accuracy using cutting-edge image analysis techniques developed by astronomers.

Traditional facial recognition software acts directly on a computer processed image to determine an identity. Using a direct image as input has bonuses such as simplicity, but drawbacks in that many theoretical and practical questions are difficult to pose on a pixel-based representation.

In a more established image analysis practice, one traditionally acts on a representation of the image that is more specific to the object of study, in this case the human face. In this compressed representation it becomes easier to ask more specific questions.

This project seeks to apply the cutting edge of such sparse Bayesian representations to the field of facial recognition, using the state-of-the-art in inference algorithms to determine the optimal and minimal unbiased representation of a human face. This project will be able to provide an orthogonal approach to currently applied techniques and could be the missing link in closing the final gap toward 100% accuracy.