A microexpression-based image recognition method, performed by an electronic device, the method comprising: inputting an original expression image belonging to a first expression type into an image augmentation model, the original expression image belonging to the first expression type being an image comprising a microexpression of a person, the image augmentation model being obtained by training with a sample expression image belonging to the first expression type and a sample expression image belonging to a second expression type, and an expression intensity of the sample expression image belonging to the second expression type being greater than an expression intensity of the sample expression image belonging to the first expression type; augmenting, in the image augmentation model, an expression feature of the microexpression of the person in the original expression image, to obtain a target expression image belonging to the second expression type; and recognizing an expression attribute type corresponding to the target expression image, and determining the expression attribute type corresponding to the target expression image as an expression attribute type corresponding to the original expression image, wherein the expression attribute type at least comprises happiness, sadness, fear, surprise, disgust, anger; wherein the image augmentation model comprises a first augmentation sub-model and a second augmentation sub-model; and the augmenting, in the image augmentation model, an expression feature of the microexpression of the person in the original expression image, to obtain a target expression image belonging to the second expression type comprises: determining an expression identification area in the original expression image as a unit original expression image, inputting the unit original expression image into the first augmentation sub-model, and augmenting an expression feature of the unit original expression image in the first augmentation sub-model, to obtain a unit auxiliary image; inputting the original expression image into the second augmentation sub-model, and augmenting an expression feature of the original expression image in the second augmentation sub-model, to obtain a target auxiliary image; and combining the unit auxiliary image and the target auxiliary image into the target expression image according to location information of the unit original expression image in the original expression image. inputting an original expression image belonging to a first expression type into an image augmentation model, the original expression image belonging to the first expression type being an image comprising a microexpression of a person, the image augmentation model being obtained by training with a sample expression image belonging to the first expression type and a sample expression image belonging to a second expression type, and an expression intensity of the sample expression image belonging to the second expression type being greater than an expression intensity of the sample expression image belonging to the first expression type; augmenting, in the image augmentation model, an expression feature of the microexpression of the person in the original expression image, to obtain a target expression image belonging to the second expression type; and recognizing an expression attribute type corresponding to the target expression image, and determining the expression attribute type corresponding to the target expression image as an expression attribute type corresponding to the original expression image, wherein the expression attribute type at least comprises happiness, sadness, fear, surprise, disgust, anger; wherein the image augmentation model comprises a first augmentation sub-model and a second augmentation sub-model; and the augmenting, in the image augmentation model, an expression feature of the microexpression of the person in the original expression image, to obtain a target expression image belonging to the second expression type comprises: determining an expression identification area in the original expression image as a unit original expression image, inputting the unit original expression image into the first augmentation sub-model, and augmenting an expression feature of the unit original expression image in the first augmentation sub-model, to obtain a unit auxiliary image; inputting the original expression image into the second augmentation sub-model, and augmenting an expression feature of the original expression image in the second augmentation sub-model, to obtain a target auxiliary image; and combining the unit auxiliary image and the target auxiliary image into the target expression image according to location information of the unit original expression image in the original expression image. determining an expression identification area in the original expression image as a unit original expression image, inputting the unit original expression image into the first augmentation sub-model, and augmenting an expression feature of the unit original expression image in the first augmentation sub-model, to obtain a unit auxiliary image; inputting the original expression image into the second augmentation sub-model, and augmenting an expression feature of the original expression image in the second augmentation sub-model, to obtain a target auxiliary image; and combining the unit auxiliary image and the target auxiliary image into the target expression image according to location information of the unit original expression image in the original expression image.