Biological cues inherent in human motion play an important role in the context of social communication. While recognizing the gender of other people is important for humans, security, advertisement and population statistics systems could also benefit from such kind of information. In this work for first time we propose a method suitable for real time gait based gender recognition relying on poses estimated from depth images. We provide evidence that pose based representation estimated by depth images could greatly benefit the problem of gait analysis. Given a gait sequence, in every frame the dynamics of gait motion are encoded using an angular representation. In particular several skeletal primitives are expressed as two Euler angles that cast votes into aggregated histograms. These histograms are then normalized, concatenated and projected onto a PCA basis in order to form the final sequence descriptor. We evaluated our method on a newly created dataset -UPCVgait - captured with Microsoft Kinect, consisting of 5 gait sequences performed by 30 subjects. An RBF kernel SVM used for classification in a leave one person out scheme on gait sequences of arbitrary length as well as on variable number of frames confirms the efficiency of our method.