Most facial verification methods assume that training and testing sets contain independent and identically distributed samples, although, in many real applications, this assumption does not hold. Whenever gathering a representative dataset in the target domain is unfeasible, it is necessary to choose one of the already available (source domain) datasets. Here, a study was performed over the differences among six public datasets, and how this impacts on the performance of the learned methods. In the considered scenario of mobile devices, the individual of interest is enrolled using a few facial images taken in the operational domain, while training impostors are drawn from one of the public available datasets. This work tried to shed light on the inherent differences among the datasets, and potential harms that should be considered when they are combined for training and testing. Results indicate that a drop in performance occurs whenever training and testing are done on different datasets compared to the case of using the same dataset in both phases. However, the decay strongly depends on the kind of features. Besides, the representation of samples in the feature space reveals insights into what extent bias is an endogenous or an exogenous factor.

Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center.