A method for processing an image, comprising: decomposing a face image into original illumination feature components and original face feature components using one of a total variation model, a logarithm total variation model and a self quotient image model, multiplying the original illumination feature components by a mean value of the original face feature components, to obtain illumination feature components denoting illumination intensity of pixel points of the face image, dividing the original face feature components by the mean value of the original face feature components, to obtain face feature components; extracting predetermined areas in the face image; calculating a maximum luminance feature, a minimum luminance feature and an illumination direction feature according to the illumination feature components in the predetermined areas; and evaluating an illumination condition in the face image, comprising: if the maximum luminance feature is less than a low luminance threshold, evaluating the illumination condition in the face image as a low luminance condition; if the minimum luminance feature is greater than a high luminance threshold, evaluating the illumination condition in the face image as a high luminance condition; and if an absolute value of the illumination direction feature is greater than a direction threshold, evaluating the illumination condition in the face image as a lateral light condition, wherein the step of calculating comprises: calculating an average illumination feature component in each area of the predetermined areas; calculating a maximum, a minimum and a mean of the average illumination feature components, as the maximum luminance feature, the minimum luminance feature and an overall mean; calculating a grayscale deviation between adjacent areas in the predetermined areas according to the average illumination feature components and the overall mean; and calculating the illumination direction feature according to the grayscale deviation. decomposing a face image into original illumination feature components and original face feature components using one of a total variation model, a logarithm total variation model and a self quotient image model, multiplying the original illumination feature components by a mean value of the original face feature components, to obtain illumination feature components denoting illumination intensity of pixel points of the face image, dividing the original face feature components by the mean value of the original face feature components, to obtain face feature components; extracting predetermined areas in the face image; calculating a maximum luminance feature, a minimum luminance feature and an illumination direction feature according to the illumination feature components in the predetermined areas; and evaluating an illumination condition in the face image, comprising: if the maximum luminance feature is less than a low luminance threshold, evaluating the illumination condition in the face image as a low luminance condition; if the minimum luminance feature is greater than a high luminance threshold, evaluating the illumination condition in the face image as a high luminance condition; and if an absolute value of the illumination direction feature is greater than a direction threshold, evaluating the illumination condition in the face image as a lateral light condition, if the maximum luminance feature is less than a low luminance threshold, evaluating the illumination condition in the face image as a low luminance condition; if the minimum luminance feature is greater than a high luminance threshold, evaluating the illumination condition in the face image as a high luminance condition; and if an absolute value of the illumination direction feature is greater than a direction threshold, evaluating the illumination condition in the face image as a lateral light condition, wherein the step of calculating comprises: calculating an average illumination feature component in each area of the predetermined areas; calculating a maximum, a minimum and a mean of the average illumination feature components, as the maximum luminance feature, the minimum luminance feature and an overall mean; calculating a grayscale deviation between adjacent areas in the predetermined areas according to the average illumination feature components and the overall mean; and calculating the illumination direction feature according to the grayscale deviation. calculating an average illumination feature component in each area of the predetermined areas; calculating a maximum, a minimum and a mean of the average illumination feature components, as the maximum luminance feature, the minimum luminance feature and an overall mean; calculating a grayscale deviation between adjacent areas in the predetermined areas according to the average illumination feature components and the overall mean; and calculating the illumination direction feature according to the grayscale deviation.