Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
This work addresses head-pose estimation for applications in social analysis and human-computer interaction, presenting an incremental improvement over existing methods.
The paper tackles robust head-pose estimation under challenging conditions like occlusions and alignment errors by proposing a mixture of linear regressions with partially-latent output, achieving competitive performance validated on three public datasets against state-of-the-art methods.
Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose tu use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available datasets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.