1. A method for image recognition utilizing an image classifier, comprising: obtaining, using a microprocessor, a skin-color or near-skin-color area of an image to be examined; and extracting, using a microprocessor, an area shape feature belonging to a feature set from the skin-color or near-skin-color area, and recognizing the image to be examined according to the area shape feature by utilizing the classifier trained using the feature set comprising the area shape feature; wherein the extracting an area shape feature belonging to a feature set from the skin-color or near-skin-color area and recognizing the image to be examined comprises: extracting at least one first area shape feature belonging to a first feature set from the skin-color or near-skin-color area, wherein the first feature set is for differentiating a positive-example sample set from a first negative-example sample set and the first negative-example sample set is a set of scene images; and recognizing whether the image to be examined is a scene image according to the at least one first area shape feature by utilizing a first classifier trained using the first feature set; if the image is not a scene image, extracting at least one second area shape feature belonging to a second feature set from the skin-color or near-skin-color area, wherein the second feature set is for differentiating the positive-example sample set from a second negative-example sample set and the positive-example sample set is a set of indecent images; and recognizing whether the image to be examined is an indecent image according to the at least one second area shape feature by utilizing a second classifier trained using the second feature set; wherein the method further comprises: before obtaining a skin-color or near-skin-color area of an image to be examined, clustering skin-color pixels in a training sample set according to a color space to obtain at least one skin-color chroma class; extracting a candidate skin-color area from a training sample, calculating a first distance between an average of chroma of the candidate skin-color area and a center of each skin-color chroma class, sorting the training sample into a skin-color chroma class whose first distance is minimal, and obtaining at least one training subset corresponding to the at least one skin-color chroma class; and calculating a skin-color probability distribution and a non-skin-color probability distribution of each training subset to obtain a skin-color probability model corresponding to each skin-color chroma class, and wherein obtaining a skin-color or near-skin-color area of an image to be examined comprises: extracting a candidate skin-color area from the image to be examined, calculating a second distance between an average of chroma of the candidate skin-color area and a center of each skin-color chroma class, making a skin-color decision for each pixel in the image to be examined according to the skin-color probability model corresponding to the skin-color chroma class whose second distance is minimal, and forming the skin-color or near-skin-color area by at least one pixel determined as in skin color according to the skin-color decision.