title: A Multi-Task Learning Framework for Head Pose Estimation under Target Motion.
abstract: Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings.
authors.forename: Yan
authors.initials: Y
authors.lastname: Yan
authors.forename: Elisa
authors.initials: E
authors.lastname: Ricci
authors.forename: Ramanathan
authors.initials: R
authors.lastname: Subramanian
authors.forename: Gaowen
authors.initials: G
authors.lastname: Liu
authors.forename: Oswald
authors.initials: O
authors.lastname: Lanz
authors.forename: Nicu
authors.initials: N
authors.lastname: Sebe
mesh_terms: D000465:Algorithms; D006257:Head; D006801:Humans; D007858:Learning; D009038:Motion; D009949:Orientation