Image processing method and device, classifier training method, and readable storage medium

An image processing device, comprising:

• a processor;
• and
• a memory, wherein the memory stores one or more computer instructions, and the one or more computer instructions, when executed by the processor, causes the processor to perform operations of:
• extracting a characteristic vector in an image to be recognized;
• based on the characteristic vector of the image to be recognized, acquiring a predicted score value of the image to be recognized;
• and
• based on the predicted score value, determining a category of an image information of the image to be recognized;
• wherein the image to be recognized is a face image, and the image information is a facial expression;
• wherein the operation of, based on the characteristic vector of the image to be recognized, acquiring the predicted score value of the image to be recognized comprises:
• by using a random-forest classifier, based on the characteristic vector of the image to be recognized, acquiring the predicted score value of the image to be recognized;
• and
• the operations further comprise:
• based on characteristic vectors and grading values of image data in a training set comprised in a dataset, training the random-forest classifier;
• wherein the dataset comprises the plurality of image data, and each of the image data is marked with a grading value;
• the grading value of each of the image data is determined according to a category of image information comprised in the each of the image data, and different categories of the image information correspond to different grading values;
• the image data are face-image data;
• and
• the training set comprises a fifth quantity of the image data in the dataset.

A training method of a random-forest classifier, comprising:

• based on characteristic vectors and grading values of image data in a training set comprised in a dataset, training the random-forest classifier;
• wherein the dataset comprises the plurality of image data, and each of the plurality of image data is marked with a grading value;
• the grading value of each of the image data is determined according to a category of image information comprised in the each of the image data, and different categories of the image information correspond to different grading values;
• the image data are face-image data, and the image information is a facial expression;
• and
• the training set comprises a fifth quantity of the image data in the dataset.