Video based face identification is a challenging problem as it needs to learn a robust model to account for face appearance change caused by pose, scale, expression and illumination variations. This paper proposes a novel video based face identification framework by combining fast sparse representation and incremental learning. For robust face identification, we proposed class specific subspace model based sparse representation which gives dense target coefficients and sparse error coefficients. Each subspace model is learned by using Principal Component Analysis (PCA). The test face is identified by using residual errors obtained for each model. For video based face identification, to harness the temporal information we integrated incremental face learning with our proposed face identification algorithm. By using reconstruction error and sparse error coefficients we have formulated new decision rules using rejection ratio and occlusion ratio respectively which helps in effective subspace model update in online. Numerous experiments using static and video datasets show that our method performs efficiently. © Springer International Publishing Switzerland 2014.