EPAT: Euclidean Perturbation Analysis and Transform - An Agnostic Data Adaptation Framework for Improving Facial Landmark Detectors

We propose EPAT, (Euclidean Perturbation Analysis and Transform) a novel unsupervised adaptation approach for improving the accuracy of any facial landmark detector by characterizing the stability of landmark prediction on test images. In EPAT, a test image is transformed several times using a set of Euclidean transforms, producing several perturbed images. The black box landmark detector is used to find facial landmarks on each perturbed version of the test image. Subsequently, inverse transforms are applied to the corresponding landmarks in order to map them back to the original image. Mean and variance are calculated for all inversely transformed detection. Mean and variance represent the new ensemble prediction and the sensitivity of the underlying landmark detector, respectively. We also introduce affine variance (AV) of facial landmarks. AV is used as a measure of the stability of the predicted landmarks and a criterion for selecting a good data adaptation model which effectively addresses potential mismatches between test and training data of the underlying landmark detector. EPAT is evaluated using four state-of-theart landmark detectors on the standard 300W dataset and also incorporated into a face recognition pipeline to show improved recognition accuracy on the challenging IJB-A dataset.