Virtual Reality (VR) applications provide an immersive experience when using panoramic images that contain a 360-degree view of the scene. Currently, the equirectangular image format is the widely used pattern to represent these panoramic images. The development of a virtual reality viewer of panoramic images should consider several parameters that define the quality of the rendered image. Such parameters include resolution configurations, texture-to-objects mappings and deciding from different rendering approach, but to select the optimal value of these parameters, visual quality analysis is required. In this work, we propose a tool integrated within Unity editor to automate this quality assessment using different settings for the visualization of equirectangular images. We compare the texture mapping of a skybox with a procedural sphere and a cubemap using full-reference objective metrics for Image Quality Analysis (IQA). Based on the assessment results, the tool decides how the final image will be rendered at the target device to produce a visually pleasing and high-quality image.

In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.