Learning Personal Specific Facial Dynamics for Face Recognition from Videos

In this paper, we present an effective approach for spatiotemporal face recognition from videos using an Extended set of Volume LBP (Local Binary Pattern features) and a boosting scheme. Among the key properties of our approach are: (1) the use of local Extended Volume LBP based spatiotemporal description instead of the holistic representations commonly used in previous works; (2) the selection of only personal specific facial dynamics while discarding the intra-personal temporal information; and (3) the incorporation of the contribution of each local spatiotemporal information. To the best of our knowledge, this is the first work addressing the issue of learning the personal specific facial dynamics for face recognition.

We experimented with three different publicly available video face databases (MoBo, CRIM and Honda/UCSD) and considered five benchmark methods (PCA, LDA, LBP, HMMs and ARMA) for comparison. Our extensive experimental analysis clearly assessed the excellent performance of the proposed approach, significantly outperforming the comparative methods and thus advancing the state-of-the-art.