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Local Binary Patterns of Spatial Plane and Temporal Linear for Spotting Spontaneous Expression Frames

Spontaneous expression can reveal people's true emotions as comparing with traditional expression. Spotting spontaneous expression frames in the video is prerequisite for studying its characteristics. This paper proposes the local binary patterns feature extraction algorithm based on spatial plane and temporal line. Firstly, the improved normalized cross-correlation algorithm is used to finely match the eye region and the mouth region respectively. Then, fusing two kinds of features, one is the mean local binary patterns features of linear region which are extracted from the temporal linear, the other is the local binary patterns features of the sector region which are extracted from the spatial plane. Finally, the feature is converted into the frame feature by feature correlation function, and frame feature of the spontaneous expression frame is larger than threshold. The experimental results show that our algorithm performs well on the CAS(ME)2 database. The hitting rate of negative spontaneous expression segments increases by 27% than the ULBP algorithm. Simultaneously, the AUC value of the ROC curve increases by 1% as comparing to the LBP-TOP algorithm.