Method and apparatus for determining image quality

An apparatus for determining image quality, 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-recognized image and facial region information obtained by performing facial recognition on the to-be-recognized image in advance and used for indicating a facial region in the to-be-recognized image;
• extracting a face image from the to-be-recognized image on the basis of the facial region information;
• inputting the face image into a pre-trained convolutional neural network to obtain probabilities of each pixel comprised in the face image belonging to a category indicated by each category identifier in a preset category identifier set, the convolutional neural network being used to represent a corresponding relationship between an image containing a face and a probability of a pixel belonging to a category indicated by a category identifier in the category identifier set, and the category identifier set including a category identifier indicating a category representing a face part;
• inputting the face image into a pre-trained key face point positioning model to obtain coordinates of each key face point comprised in the face image, the key face point positioning model being used for representing a corresponding relationship between an image containing a face and coordinates of each key face point;
• determining a probability of the face image being obscured on the basis of the probabilities and the coordinates;
• and
• determining whether the quality of the face image is up to standard on the basis of the probability of the face image being obscured.

A non-transitory computer storage medium storing a computer program, the computer program when executed by one or more processors, causes the one or more processors to perform operations, the operations comprising:

• acquiring a to-be-recognized image and facial region information obtained by performing facial recognition on the to-be-recognized image in advance and used for indicating a facial region in the to-be-recognized image;
• extracting a face image from the to-be-recognized image on the basis of the facial region information;
• inputting the face image into a pre-trained convolutional neural network to obtain probabilities of each pixel comprised in the face image belonging to a category indicated by each category identifier in a preset category identifier set, the convolutional neural network being used to represent a corresponding relationship between an image containing a face and a probability of a pixel belonging to a category indicated by a category identifier in the category identifier set, and the category identifier set including a category identifier indicating a category representing a face part;
• inputting the face image into a pre-trained key face point positioning model to obtain coordinates of each key face point comprised in the face image, the key face point positioning model being used for representing a corresponding relationship between an image containing a face and coordinates of each key face point;
• determining a probability of the face image being obscured on the basis of the probabilities and the coordinates;
• and
• determining whether the quality of the face image is up to standard on the basis of the probability of the face image being obscured.