The invention discloses a multi-channel network-based video human face detection and identification method. The method comprises the following steps of S1, performing video preprocessing: adding time information to each frame image; S2, detecting a target human face and calculating a pose coefficient; S3, correcting a human face pose: for m human faces obtained in the step S2, performing pose adjustment; S4, extracting human face features based on a deep neural network; and S5, comparing the human face features: for an input human face, obtaining eigenvectors by utilizing the step S4, matching a matching degree of an eigenvector of the input human face and a vector in a feature library by utilizing a cosine distance, and adding a class to alternative classes, and if the cosine distances between a feature of the to-be-identified human face and central features of all classes are all smaller than a set threshold phi, regarding that a database does not store information of a person, and ending the identification, wherein the cosine distance between the class and the to-be-identified human face is greater than the set threshold phi. The multi-channel network-based video human face detection and identification method with relatively high accuracy is provided.