Facial micro-expressions (MEs) are fast and involuntary facial expressions which reveal people hidden emotions. ME spotting refers to the process of finding the temporal locations of rapid facial movements from a video sequence. However, detecting these events is difficult due to their short durations and low intensities. Also, a distinction must be made between MEs and eye-related movements (blinking, eye-gaze change, etc). Taking inspiration from video magnification techniques, we design a workflow for automatically spotting MEs based on the Riesz pyramid. In addition , we propose a filtering and masking scheme that segment motions of interest without producing undesired arti-facts or delays. Furthermore, the system is able to differentiate between MEs and eye movements. Experiments are carried out on two databases containing videos of spontaneous micro-expressions. Finally, we show that our method is able to outperform other methods from the state of the art in this challenging task.