Aparna Bharati

TL;DR: To detect retouching in face images, a novel supervised deep Boltzmann machine algorithm is proposed that uses facial parts to learn discriminative features to classify face images as original or retouched with high accuracy.

TL;DR: An end-to-end processing pipeline for image provenance analysis which works at real-world scale is presented, employing a cutting-edge image filtering solution that is custom-tailored for the problem at hand, as well as novel techniques for obtaining the provenance graph that expresses how the images, as nodes, are ancestrally connected.

TL;DR: The scope and applicability of metadata-based inferences for provenance graph construction in two different scenarios: digital image forensics and cultural analytics are tested.

TL;DR: The top verification rate for PaSC from this evaluation is 0.98 at a false accept rate of 0.01 - a remarkable advancement in performance from the competition held at FG 2015.

TL;DR: In this article, the authors evaluate a number of facial landmarking algorithms and a popular frontalization method to understand their effect on facial recognition performance, and introduce a new, automatic, single image frontalization scheme that exceeds the performance of the reference frontalization algorithm for video-to-video face matching on the Point and Shoot Challenge (PaSC) dataset.