A comparative study of face landmarking techniques

Abstract

Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important intermediary step for many subsequent face processing operations that range from biometric recognition to the understanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their progress over the last decade, categorize them and show comparative performance statistics of the state of the art. We discuss the main trends and indicate current shortcomings with the expectation that this survey will provide further impetus for the much needed high-performance, real-life face landmarking operating at video rates.

Persistent homology to analyse 3D faces and assess body weight gain

“…A landmark-free approach would also give the advantage of not having to pre-compute face landmarks. Although landmarks can be identified with different strategies [10], locating landmarks with the desired accuracy could be difficult on bad-quality face scans. The future work also include the use of hybrid descriptors which analyse both shape and texture, as for example the hybrid geodesic distance in [6].…”

Can Your Eyes Tell Me How You Think? A Gaze Directed Estimation of the Mental Activity

“…Yet, the method specifics place it close to iris center localization, gaze detection or facial landmark estimation. Despite the rich computer vision literature in all these aspects, due to paper length limitations, we will consider only a partial review and we refer the reader to the recent surveys in [17] (for eye localization and gaze tracking) and in [8] (for facial landmark estimation).…”

Shape-appearance-correlated active appearance model

“…d is the Euclidean distance between the k-th landmark of the ground-truth face and the corresponding detected landmark of the i-th test face; and IOD is the Inter-Ocular Distance, which is the distance between the two eye pupils. According to the measurement in [26],…”

“…To measure the performances of the algorithms, we use the ground-truth-based localization error, as in [26] [9], which is the point-to-point error, normalized by the eye distance. Given the ground-truth landmarks, the localization error k i e is computed as follows:…”