In recent academic literature Sex and Gender have both become synonyms, even though distinct definitions doexist. This give rise to the question, which of those two are actually face image classifiers identifying? It will beargued and explained why CNN based classifiers will generally identify gender, while feeding face recognitionfeature vectors into a neural network, will tend to verify sex rather than gender. It is shown for the first time howstate of the art Sex Classification can be performed using Embedded Prototype Subspace Classifiers (EPSC) andalso how the projection depth can be learned efficiently. The automatic Gender classification, which is producedby the InsightFace project, is used as a baseline and compared to the results given by the EPSC, which takes thefeature vectors produced by InsightFace as input. It turns out that the depth of projection needed is much largerfor these face feature vectors than for an example classifying on MNIST or similar. Therefore, one importantcontribution is a simple method to determine the optimal depth for any kind of data. Furthermore, it is shown howthe weights in the final layer can be set in order to make the choice of depth stable and independent of the kind oflearning data. The resulting EPSC is extremely light weight and yet very accurate, reaching over 98% accuracy forseveral datasets.