We documented the relabeling process for a subset of a renowned database for emotion-in-context recognition, with the aim of promoting reliability in final labels. To this end, emotion categories were organized into eight groups, while a large number of participants was requested for tagging. A strict control strategy was performed along the experiments, whose duration was 13.45 minutes average per day. Annotators were free to participate in any of the daily experiments (the average number of participants was 28), and a Z-Score filtering technique was implemented to keep trustworthiness of annotations. As a result, the value of the agreement parameter Fleiss’ Kapa increasingly varied from slight to almost perfect, revealing a coherent diversity of the experiments. Our results support the hypothesis that a small number of categories and a large number of voters benefit reliability of annotations in contextual emotion imagery.

Dried droplet imaging has become an established approach for analyzing complex fluid systems as evaporation patterns preserve physicochemical and structural information. However, spatial variability of these patterns limits the effectiveness of conventional texture descriptors. This work presents a region-aware deep learning algorithm for the classification of dried droplets through spatial decomposition and convolutional learning. Dried droplet images were geometrically standardized prior to patch extraction. The Hough transform supported consistent crown–core localization and patch extraction across the dataset. Each image was divided into twenty localized regions. Twelve represented peripheral patches sampled at 30° intervals. The remaining eight patches were extracted from four central regions using two complementary angular orientations (30° and 90°) per region. This process allowed dataset augmentation and evaluation of regional contributions. Patches were processed using the VGG16 convolutional neural network, following repeated patch-level and droplet-level partitioning strategies designed to prevent data leakage. The proposed algorithm was evaluated on Methotrexate (MTX) droplets prepared with 40%, 60%, and 80% water dilution levels relative to a reference solution. Results show that the proposed strategy increases classification accuracy, outperforming traditional descriptors. These findings demonstrate that spatial decomposition combined with convolutional learning constitutes an effective approach for heterogeneous evaporative pattern classification.