8,455 research outputs found
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Recommended from our members
Virtual viewpoint three-dimensional panorama
Conventional panoramic images are known to provide for an enhanced field of view in which the scene
always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual
viewpoint creation to generate different panoramic images of the same scene with three-dimensional
component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of
panoramic images
Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality
This paper introduces a method of structure inspection using mixed-reality
headsets to reduce the human effort in reporting accurate inspection
information such as fault locations in 3D coordinates. Prior to every
inspection, the headset needs to be localized. While external pose estimation
and fiducial marker based localization would require setup, maintenance, and
manual calibration; marker-free self-localization can be achieved using the
onboard depth sensor and camera. However, due to limited depth sensor range of
portable mixed-reality headsets like Microsoft HoloLens, localization based on
simple point cloud registration (sPCR) would require extensive mapping of the
environment. Also, localization based on camera image would face the same
issues as stereo ambiguities and hence depends on viewpoint. We thus introduce
a novel approach to Joint Point Cloud and Image-based Localization (JPIL) for
mixed-reality headsets that use visual cues and headset orientation to register
small, partially overlapped point clouds and save significant manual labor and
time in environment mapping. Our empirical results compared to sPCR show
average 10 fold reduction of required overlap surface area that could
potentially save on average 20 minutes per inspection. JPIL is not only
restricted to inspection tasks but also can be essential in enabling intuitive
human-robot interaction for spatial mapping and scene understanding in
conjunction with other agents like autonomous robotic systems that are
increasingly being deployed in outdoor environments for applications like
structural inspection
- …