Pose invariant facial component-landmark detection

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.