Methods and systems for image processing using a learning engine

A computer-implemented method comprising:

• under control of a hardware computing device configured with specific computer-executable instructions:
• accessing a data training set comprising images of different faces;
• accessing a first autoencoder configuration, comprising:
• a first encoder, and
• a first decoder;
• training the first autoencoder configuration comprising the first encoder and the first decoder using dataset images, wherein node weights associated with the first encoder and node weights associated with the first decoder are modified during the training of the first autoencoder configuration;
• accessing a second autoencoder configuration, comprising:
• the first encoder, and
• a second decoder;
• training with respect to a first target face, using a plurality of images of the first target face, the second decoder in the second autoencoder configuration,
• wherein node weights associated with the first encoder are fixed, wherein node weights associated with the second decoder are modified;
• using an autoencoder comprising:
• the first encoder, trained using the using dataset images, and
• the trained second decoder, trained using the plurality of images of the first target face,
• to generate an output using a source image of a first face having a facial expression, where the facial expression of the first face from the source image is applied to the first specific target face.

An electronic image processing system, comprising:

• at least one computing device;
• computer readable memory including instructions operable to be executed by the at least one computing device to perform a set of actions, configuring the at least one computing device to perform operations comprising:
• access a data training set comprising images of different faces;
• access a first autoencoder configuration, comprising:
• a first encoder, and
• a first decoder;
• train the first autoencoder configuration comprising the first encoder and the first decoder using dataset images, wherein node weights associated with the first encoder and node weights associated with the first decoder are modified during the training of the first autoencoder configuration;
• access a second autoencoder configuration, comprising:
• the first encoder, and
• a second decoder;
• train with respect to a first target face, using a plurality of images of the first target face, the second decoder in the second autoencoder configuration,
• wherein node weights associated with the first encoder are substantially maintained, wherein node weights associated with the second decoder are modified;
• use an autoencoder comprising:
• the first encoder, trained using the dataset images, and
• the second decoder, trained using the plurality of images of the first target face,
• to generate an output using a source image of a first face having a facial expression, where the facial expression of the first face from the source image is applied to the first specific target face.