The invention requests to protect an identification method for a human
facial expression based on two-step
dimensionality reduction and parallel
feature fusion. The adopted two-step dimensionality method comprises the following steps: firstly, respectively performing the first-time
dimensionality reduction on two kinds of human
facial expression features to be fused in the real number field by using a
principal component analysis (PCA) method, then performing the parallel
feature fusion on the features subjected to
dimensionality reduction in a unitary space, secondly, providing a
hybrid discriminant analysis (HDA) method based on the unitary space as a feature dimensionality reduction method of the unitary space, respectively extracting two kinds of features of a local binary pattern (LBP) and a
Gabor wavelet, combining dimensionality reduction frameworks in two steps, and finally, classifying and training by adopting a
support vector machine (SVM). According to the method, the dimensions of the parallel fusion features can be effectively reduced; besides, the identification for six kinds of human facial expressions is realized and the
identification rate is effectively improved; the defects existing in the identification method for serial
feature fusion and single feature expression can be avoided; the method can be widely applied to the fields of mode identification such as safe
video monitoring of public places,
safe driving monitoring of vehicles, psychological study and medical monitoring.