The invention discloses a
mask face
living body detection method based on a
support vector machine, and the method comprises the steps: carrying out the positioning of face key points through a face key point positioning
algorithm based on a multi-stage
cascade regression tree, and automatically generating
mask face samples in batches according to the positioning points; constructing a face
library without a
mask and a face
library with a mask; performing face recognition; extracting an LBP feature of the face image, and converting an LBP
feature matrix into a
histogram vector as a
texture feature vector for
living body detection; training and storing a
support vector machine model based on texture features; carrying out
living body detection; and outputting a
verification result according to the face recognition result and the living body detection result. The face key
point model is trained, key points of the
nose bridge and the
chin of the face can be positioned, and whether the face wears a mask or not is judged by detecting whether the key points are shielded or not; according to the method, the
support vector machine is trained, a rapid in-vivo detection function is realized, the function is simultaneously suitable for an ordinary face and a face with a mask, the
rapidity and accuracy of face recognition are not influenced, and the safety of the
system is improved.