The present application belongs to the technical field of data representation and
signal classification, and relates to a face expression classification method and
system. The
system comprises a facial bioelectricity sensor, a hardware
data noise filtering module and an upper computer. The facial bioelectricity sensor is used to collect facial
electromyography, electrooculography signals and posture data. The hardware
data noise filtering module is used to process the
electromyography and electrooculography signals. The method extracts
time domain,
frequency domain and transform domain features from the digital
electromyography and electrooculography data after digital-analog conversion. The features extracted are cross-correlated and fused after
correlation coefficient calculation and judgment. After the average MIC is calculated, the selected data is classified to obtain the expression type. The method and
system can increase the number and type of significant features by cross-correlating and fusing the features according to the
correlation coefficient and threshold value. After statistics, screening and alignment, the classification is more accurate than the existing method and system.