Facial image clustering in stereo videos using local binary patterns and double spectral analysis
In this work we propose the use of local binary patterns in combination with double spectral analysis for facial image clustering applied to 3D (stereoscopic) videos. Double spectral clustering involves the fusion of two well known algorithms: Normalized cuts and spectral clustering in order to improve the clustering performance. The use of local binary patterns upon selected fiducial points on the facial images proved to be a good choice for describing images. The framework is applied on 3D videos and makes use of the additional information deriving from the existence of two channels, left and right for further improving the clustering results.