Video is a recursively measured signal where frames are highly correlated with structured sparsity and low-rankness. A simple example is facial expression - multiple measurements of a face. Several salient facial action units (AU) are often enough for a correct expression recognition. We hope that AUs are not stored when the face remains neutral until they become salient when expression occurs, as well as that the recognizer is still able to restore historic salient AUs. A temporal memory mechanism is appealing for a real-time system to reduce rich redundancy in information coding. We formulate expression recognition as a video Sparse Representation based Classification (SRC) with Long Short-Term Memory (LSTM) mechanism, which is applicable for human actions yet requiring a careful design of sparse representation due to possible changing scenes. Preliminary experiments are conducted on the MPI Face Video Database (MPI-VDB). We compare the proposed sparse coding with temporal modeling using LSTM against the baseline of sparse coding with simultaneous recursive matching pursuit (SRMP).
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