Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications. INDEX TERMS Iris center localization, image gradient, image inpaint, energy map synthesis, post-processing correction.

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.

Driving energy consumption plays an important role in the navigation of autonomous mobile robots in off-road scenarios. However, the accuracy of the driving energy predictions is often affected by a high degree of uncertainty due to unknown and constantly varying terrain properties, and the complex wheel-terrain interaction in unstructured terrains. In this paper, a probabilistic deep meta-learning approach is proposed to model the existing uncertainty in the driving energy consumption and efficiently adapt the probabilistic predictions based on a small number of local measurements. The method expands upon an existing deterministic deep-meta learning model that, in contrast, only provided single-point energy estimates. The performance of the proposed method is compared against the deterministic approach in a 3D-body dynamic simulator over several typologies of deformable terrains and unstructured geometries. In this way, the benefits of the proposed method are illustrated to enhance the predictions with informative probabilistic considerations, which can be crucial to the safety of mobile robots traversing challenging, unstructured environments.INDEX TERMS Autonomous mobile robots, deep learning, energy-aware path planning, meta-learning, probabilistic neural networks