Method and apparatus for generating information

An apparatus for generating information, comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• acquiring a to-be-detected image and facial region information used to indicate a facial region in the to-be-detected image and obtained by previously performing a face detection on the to-be-detected image, the facial region information comprising a coordinate of a center point or a vertex of a facial region, and a height and a width of the facial region;
• extracting a facial image from the to-be-detected image based on the facial region information;
• inputting the facial image into a pre-trained convolutional neural network to obtain image feature information, the convolutional neural network being used to extract an image feature;
• analyzing the image feature information to determine a probability that each pixel point included in the facial image belongs to a category indicated by each category identifier in a, based on a correspondence table storing a correspondence between the image feature information and the probability that each pixel point belongs to the category indicated by each category identifier in the preset category identifier set;
• and
• generating information for indicating the category to which each pixel point belongs based on the probability.

A non-transitory computer storage medium, storing a computer program, wherein the program, when executed by a processor, cause the processor to perform operations, the operations comprising:

• acquiring a to-be-detected image and facial region information used to indicate a facial region in the to-be-detected image and obtained by previously performing a face detection on the to-be-detected image, the facial region information comprising a coordinate of a center point or a vertex of a facial region, and a height and a width of the facial region;
• extracting a facial image from the to-be-detected image based on the facial region information;
• inputting the facial image into a pre-trained convolutional neural network to obtain image feature information, the convolutional neural network being used to extract an image feature;
• analyzing the image feature information to determine a probability that each pixel point included in the facial image belongs to a category indicated by each category identifier in a preset category identifier set, based on a correspondence table storing a correspondence between the image feature information and the probability that each pixel point belongs to the category indicated by each category identifier in the preset category identifier set;
• and
• generating information for indicating the category to which each pixel point belongs based on the probability.