. "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."@en . "Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?"@en . "3"^^ . "Mr\u00E1zov\u00E1, Iveta" . . "3"^^ . . . "2161-4407" . . . "2013" . "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6707101" . "8"^^ . . . "Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?" . . "11320" . . . "P(GAP103/10/0783), P(GAP202/10/1333)" . "64178" . . . "10.1109/IJCNN.2013.6707101" . "RIV/00216208:11320/13:10173963!RIV14-GA0-11320___" . "Pihera, Josef" . . "Proceedings of The 2013 International Joint Conference on Neural Networks (IJCNN)" . "Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?" . . "Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?"@en . . "Velem\u00EDnsk\u00E1, Jana" . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . . "August 2013" . "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." . "sexual dimorphism; facial scans; classification; internal knowledge representation; convolutional neural networks"@en . . . . . "RIV/00216208:11320/13:10173963" . "[2FA8A71E019B]" .