Supplementary research data to the paper (rejected): Davide Fantini, Federico Avanzini, Stavros Ntalampiras and Giorgio Presti (2023) "Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane" The repository includes the research data generated and analyzed in the abovementioned paper describing a method for PRTF individualization. In particular, the following data are included: README.md: instructions for the data pinna_range_img.mat: pinna range images extracted from the 3D head meshes of the HUTUBS dataset landmarks.mat: landmarks coordinates both manually annotated and automatically placed with ASM anthropometry.mat: anthropometric parameters automatically extracted from both manually annotated and ASM-fitted landmarks img_features.mat: image features pinna cavities extracted from both manually annotated and ASM-fitted landmarks grnn_models.mat: Generalized Regression Neural Network (GRNN) models trained from both HUTUBS anthropometry and the proposed pinna features predicted_dtf.mat: Directional Transfer Function (DTF) sets predicted from both HUTUBS anthropometry and the proposed pinna features anthropometry_documentation.pdf: documentation of the pinna anthropometric parameters auditory_model_complete_elevation_range.pdf: auditory model evaluation in the complete elevation range The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (scipy.io.loadmat). A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available here.