A computer-implemented image processing method, comprising: obtaining a two-dimensional target face image; receiving an identification curve marked by a user in the target face image, wherein the identification curve is used to indicate face contour distribution in the target face image and the face contour distribution comprises a shape of a face contour, a position of a face contour, or a trend of a face contour curve; locating a facial contour curve of a face from the target face image according to the identification curve and by using an image over-segmentation of said target face image; determining a three-dimensional posture of the face in the target face image comprising the steps of: detecting a face area in the target face image; performing binarization feature extraction in the face area; and performing regression prediction according to an extracted binarization feature and by using a preset posture regression prediction function, to obtain the three-dimensional posture of the face in the target face image; detecting a feature point position of the face in the target face image comprising the steps of: determining a face area in the target face image; performing, according to a preset face initial feature point position, binarization feature extraction in a specified range of the initial feature point position in the face area; and performing regression prediction according to an extracted binarization feature by using a preset feature point regression prediction function, to obtain the feature point position of the face in the target face image; and constructing a three-dimensional shape of the face in the target face image according to the facial contour curve, the three-dimensional posture, and the feature point position of the face in the target face image by using a preset empirical model of a three-dimensional face shape and a target function matching the empirical model of the three-dimensional face shape, wherein the target function is used to determine a deformation coefficient of the empirical model according to the three-dimensional posture, the facial contour curve, and the feature point position of the face in the target face image, and mappings of the facial contour curve and the feature point position of the face onto three-dimensional space. obtaining a two-dimensional target face image; receiving an identification curve marked by a user in the target face image, wherein the identification curve is used to indicate face contour distribution in the target face image and the face contour distribution comprises a shape of a face contour, a position of a face contour, or a trend of a face contour curve; locating a facial contour curve of a face from the target face image according to the identification curve and by using an image over-segmentation of said target face image; determining a three-dimensional posture of the face in the target face image comprising the steps of: detecting a face area in the target face image; performing binarization feature extraction in the face area; and performing regression prediction according to an extracted binarization feature and by using a preset posture regression prediction function, to obtain the three-dimensional posture of the face in the target face image; detecting a face area in the target face image; performing binarization feature extraction in the face area; and performing regression prediction according to an extracted binarization feature and by using a preset posture regression prediction function, to obtain the three-dimensional posture of the face in the target face image; detecting a feature point position of the face in the target face image comprising the steps of: determining a face area in the target face image; performing, according to a preset face initial feature point position, binarization feature extraction in a specified range of the initial feature point position in the face area; and performing regression prediction according to an extracted binarization feature by using a preset feature point regression prediction function, to obtain the feature point position of the face in the target face image; and determining a face area in the target face image; performing, according to a preset face initial feature point position, binarization feature extraction in a specified range of the initial feature point position in the face area; and performing regression prediction according to an extracted binarization feature by using a preset feature point regression prediction function, to obtain the feature point position of the face in the target face image; and constructing a three-dimensional shape of the face in the target face image according to the facial contour curve, the three-dimensional posture, and the feature point position of the face in the target face image by using a preset empirical model of a three-dimensional face shape and a target function matching the empirical model of the three-dimensional face shape, wherein the target function is used to determine a deformation coefficient of the empirical model according to the three-dimensional posture, the facial contour curve, and the feature point position of the face in the target face image, and mappings of the facial contour curve and the feature point position of the face onto three-dimensional space.