Facial image generating method, facial image generating apparatus, and facial image generating device

A facial image generating apparatus, comprising:

• a facial feature generating module configured to:
• generate an M-dimensional facial feature vector, M being an integer larger than one, and
• extract the facial feature vector from a given facial image by use of a deep convolutional network for facial feature extraction,
• wherein the deep convolutional network for facial feature extraction comprises:
• P layers of convolutional neural network and at least one layer of fully connected neural network, P being an integer larger than or equal to two,
• wherein a first layer of convolutional neural network is used to receive the given facial image, the at least one layer of fully connected neural network is used to receive images outputted from a P-th layer of convolutional neural network and generate the facial feature vector;
• a vector synthesizing module configured to link the M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate an (M+N)-dimensional synthesized feature vector, N being an integer larger than or equal to one;
• and
• a synthesized facial image generating module configured to generate a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector.

A facial image generating device, comprising:

• one or more processors;
• one or more memories for storing program instructions, the program instructions being executed by the processors to perform the following steps:
• generating an M-dimensional facial feature vector, M being an integer larger than one;
• linking the M-dimensional facial feature vector with an N-dimensional demanded feature vector to generate an (M+N)-dimensional synthesized feature vector, N being an integer larger than or equal to one;
• and
• generating a synthesized facial image by use of a deep convolutional network for facial image generation and based on the synthesized feature vector,
• wherein generating the facial feature vector comprises:
• extracting the facial feature vector from a given facial image by use of a deep convolutional network for facial feature extraction;
• or
• randomly generating the facial feature vector,
• wherein in the case of extracting the facial feature vector from the given facial image, the processors execute the program instructions further for:
• generating a face matching score based on the synthesized facial image and the given facial image and by use of a first deep convolutional network for face determination;
• and
• updating parameters of the deep convolutional network for facial feature extraction, the deep convolutional network for facial image generation, and the first deep convolutional network for face determination based on the face matching score;
• or
• in the case of randomly generating the facial feature vector, the processors execute the program instructions further for:
• generating a face satisfaction score based on the synthesized facial image and by use of a second deep convolutional network for face determination;
• and
• updating parameters of the deep convolutional network for facial image generation and the second deep convolutional network for face determination based on the face satisfaction score.