In this work, we are focusing on facial image clustering techniques applied on stereoscopic videos. We introduce a novel spectral clustering algorithm which combines two well-known algorithms: normalized cuts and spectral clustering. Furthermore, we introduce two approach for evaluating the similarities between facial images, one based on Mutual Information and other based on Local Binary Patterns, combined with facial fiducial points and an image registration procedure. Ways of exploring the extra information available in stereoscopic videos are also introduced. The proposed approaches are successfully tested on three stereoscopic feature films and compared against the state-of-the-art.