Showing papers by "Manos Papadakis published in 2011"

Facial component-landmark detection

Abstract: Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By “component” we refer to a rectangular subregion of the face, containing an anatomical component (e.g., “eye”). We present a fully-automated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bag-of-words descriptors incorporated into a cascade of boosted classifiers. Specifically, first each component-landmark detector is applied independently and then the information obtained is used to make inferences for the localization of multiple components. The advantage of our approach is that it has robustness to pose as well as illumination. Our method has a failure rate lower than that of commercial software. Additionally, we demonstrate that using our method for the initialization of a point landmark detector results in performance comparable with that of state-of-the-art methods. All of our experiments are carried out using data from a publicly available database.

Pose invariant facial component-landmark detection

Abstract: Facial landmark detection has proved to be a very challenging task in biometrics due to the numerous sources of variation In this work, we present an algorithm for robust detection of facial component-landmarks Specifically, we address the variation due to extreme pose and illumination To achieve robust detection for extreme poses, we use a set of independent pose and landmark specific detectors Each component-landmark detector is applied independently and the information obtained is used to make inferences about the layout of multiple components In addition, we incorporate a multi-view representation based on an aspect graph approach The performance of our algorithm is assessed using data from a publicly available database The failure rate of our method is lower than that of commercially available software

3D-rigid motion invariant discrimination and classification of 3D-textures

Abstract: In this paper we implement a method for the 3D-rigid motion invariant texture discrimination and binary classification for discrete 3D-textures that are spatially homogeneous by modelling them as stationary Gaussian random fields. We use a novel 'distance' between 3D-textures that remains invariant under all 3D-rigid motions of the texture to develop rules for 3D-rigid motion invariant texture discrimination and binary classification of textures.