Active Pictorial Structures

Epameinondas Antonakos, Joan Alabort-i-Medina, Stefanos Zafeiriou; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5435-5444


In this paper we present a novel generative deformable model motivated by Pictorial Structures (PS) and Active Appearance Models (AAMs) for object alignment in-the-wild. Inspired by the tree structure used in PS, the proposed Active Pictorial Structures (APS) model the appearance of the object using multiple graph-based pairwise normal distributions (Gaussian Markov Random Field) between the patches extracted from the regions around adjacent landmarks. We show that this formulation is more accurate than using a single multivariate distribution (Principal Component Analysis) as commonly done in the literature. APS employ a weighted inverse compositional Gauss-Newton optimization with fixed Jacobian and Hessian that achieves close to real-time performance and state-of-the-art results. Finally, APS have a spring-like graph-based deformation prior term that makes them robust to bad initializations. We present extensive experiments on the task of face alignment, showing that APS outperform current state-of-the-art methods. To the best of our knowledge, the proposed method is the first weighted inverse compositional technique that proves to be so accurate and efficient at the same time.

Related Material

author = {Antonakos, Epameinondas and Alabort-i-Medina, Joan and Zafeiriou, Stefanos},
title = {Active Pictorial Structures},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}