Section: New Results

Head Pose Estimation and Tracking

Head pose estimation is an important task, because it provides information about cognitive interactions that are likely to occur. Estimating the head pose is intimately linked to face detection. We addressed the problem of head pose estimation with three degrees of freedom (pitch, yaw, roll) from a single image and in the presence of face detection errors. Pose estimation is formulated as a high-dimensional to low-dimensional mixture of linear regression problem [7]. We propose a method that maps HOG-based descriptors, extracted from face bounding boxes, to corresponding head poses. To account for errors in the observed bounding-box position, we learn regression parameters such that a HOG descriptor is mapped onto the union of a head pose and an offset, such that the latter optimally shifts the bounding box towards the actual position of the face in the image. The performance of the proposed method is assessed on publicly available datasets. The experiments that we carried out show that a relatively small number of locally-linear regression functions is sufficient to deal with the non-linear mapping problem at hand. Comparisons with state-of-the-art methods show that our method outperforms several other techniques [30]. This work is part of the PhD of Vincent Drouard [28] that received the best student paper award (second place) at the IEEE ICIP'15.

In 2017 we extended this work and we proposed a head-pose tracker based on a switching Kalman filter (SKF) formalism. The SKF governs the temporal predictive distribution of the pose parameters (modeled as continuous latent variables) conditioned by the discrete variables associated with the mixture of linear inverse-regression formulation of [7]. We formally derived the equations of the proposed switching linear regression model, we proposed an approximation that is both identifiable and computationally tractable, we designed an EM procedure to estimate the SKF parameters in closed-form, and we carried out experiments and comparisons with other methods using recently released datasets [40].

Websites:

https://team.inria.fr/perception/research/head-pose/

https://team.inria.fr/perception/research/head-pose-tracking/