We extracted individual and gender features from range data on the human nose measured by a three-dimensional digitizer. We propose extracting individual and gender features from range data measured for the nose based on a solid model, which gives feature vectors for volume, length between vertexes, and angles around vertex. We determined elements of feature vectors by statistical analysis and authentication tests. We achieved an 83.67% individual identification rate and a 98.01% gender identification rate, verifying the effectiveness of our proposed method.