The invention discloses a method for identifying human faces based on an HMM-SVM
hybrid model, which comprises the following steps: firstly, sampling human face images from top to bottom by sampling windows; extracting characteristic parameters of each sampling window image by respectively adopting
discrete cosine transform (DCT) and
singular value decomposition (SVD), and serially connecting the characteristic parameters into one-dimensional observation vectors; then, using the observation vectors of the training images of each
human body to
train the HMM model of each
human body; adopting the
Viterbi algorithm to calculate the output probability of the observation vectors of all images corresponding to each HMM model; and using the output probability to support the classified training and the identification test of a vector
machine. Because each HMM model has good
time sequence modeling ability, the numerical characteristics of each organ of a human face can be effectively combined by a state
transfer model to more integrally describe the human face to support the excellent performance of the vector
machine in the aspect of classification of limited samples.