Abstract
A growing availability of high-dimensional object data, e.g., from medicine or forensic analysis motivated us to develop a new variant of classical convolutional neural networks. The introduced model of N-dimensional convolutional neural networks (ND-CNN) enhanced with an enforced internal knowledge representation allows to process general N-dimensional object data while supporting adequate interpretation of the found object characteristics.
Experimental results obtained so far for gender classification of 3D face scans confirm an extremely strong power of the proposed neural classifier. The developed ND-CNNs significantly outperformed humans (by 33%) while still allowing for a transparent representation of the face features present and detected in the data.