Patent ID: 11922707
Assignee: BIGO TECHNOLOGY PTE. LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. An apparatus for training a face detection model, comprising:
a training face image acquiring module configured to acquire a training face image;
a three-dimensional reconstruction module configured to perform three-dimensional reconstruction on the training face image based on a preset three-dimensional face model and acquire a training three-dimensional face model;
a training UV coordinate map generating module configured to generate a training UV coordinate map comprising three-dimensionalcoordinates of the training three-dimensional face model based on the training three-dimensional face model, wherein U is a horizontal coordinate and V is a vertical coordinate; and
a training module configured to train a semantic segmentation network by using the training face image and the training UV coordinate map and acquire a face detection model, wherein the face detection model is configured to generate a UV coordinate map comprising three-dimensional coordinates;
wherein the training module comprises:
a training face image extracting sub-module configured to extract the training face image;
a predicted UV coordinate map extracting sub-module configured to input the training face image into a semantic segmentation network so as to extract a predicted UV coordinate map;
a loss rate calculating sub-module configured to calculate a loss rate of the predicted UV coordinate map by using a preset loss function and the training UV coordinate map;
a gradient calculating sub-module configured to calculate a gradient using the loss rate;
an iteration condition judging sub-module configured to judge whetherthe gradient satisfies a preset iteration condition;
a face detection model determining sub-module configured to determine the semantic segmentation network as a face detection model;
a network parameter adjusting sub-module configured to perform gradient descent on a network parameter of the semantic segmentation network using the gradient and a preset learning rate, and return to the step of extracting the training face image; and
wherein the three-dimensional reconstruction module comprises:
a three-dimensional face model selecting sub-module configured to select M three-dimensional face models;
a principal component analyzing sub-module is configured to acquire a principal component matrix and an eigenvalue matrix by performing principal component analysis on the M three-dimensional face models; and
a three-dimensional reconstruction sub-module configured to, for each training face image, acquire the training three-dimensional face model by performing three-dimensional reconstruction on each training face image using the principal component matrix and the eigenvalue matrix.