The challenges of recognition of spontaneous expressions from spatio-temporal data include the characterization of subtle changes of facial textures, which in many cases occur for a very brief duration. In this context, the paper presents an intelligent approach for spontaneous expression recognition algorithm, wherein adaptive magnification of motion of spatio-temporal data is applied prior to the extraction of features of expression. The proposed magnification enhances the low-intensity facial activities without introducing notable artifacts for the high-intensity activities. The local binary patterns extracted from three-orthogonal planes of the Eulerian magnified spatio-temporal data are used as features of spontaneous expressions. The extracted features are classified using the well-known support vector machine classifier. Experiments are conducted on commonly-referred spatio-temporal databases such as the SMIC and MMI that have spontaneous expressions representing the micro- and meso-level facial activities, respectively. Experimental results reveal that the proposed approach of motion magnification prior to feature extraction significantly improves the detection and classification accuracy at the expense of acceptable robustness.
['Bashir M. Sabquat Bahar Talukder', 'Brinta Chowdhury', 'Tamanna Howlader', 'S. M. Rahman']
Intelligent Recognition of Spontaneous Expression Using Motion Magnification of Spatio-temporal Data