This thesis is aimed at developing an algorithm able to recognize people instances in grayscale pictures. This is a One-Class Classification problem where objects of a particular class are identified compared to all other possible ones. The person class is called positive class or target class, while other items are referred to be in the negative class, also called alien class. The biggest challenge is represented by the variety of objects opposed to the target class, which does neither allow to model the external class in a univocal way, nor to have all possible cases inside the training set. This problem cannot be solved using traditional techniques of binary and multi-class classifications, precisely because there are no pre-defined classes, so no labeled data different from target class objects are available.