1. A learning device comprising: feature-point extracting means for extracting feature points as characteristic points from one of a plurality of generation images including a positive image in which a predetermined identification object is viewed and a negative image in which no identification object is viewed for use in learning to generate an identifier for identifying whether or not a subject viewed in an image is the identification object; feature-point feature-quantity extracting means for extracting feature-point feature-quantities representing features of the feature points of the generation image; total-feature-quantity generating means for generating a total feature quantity represented by a multi-dimensional vector from the feature-point feature-quantities of the generation image, wherein the total feature quantity represents a feature of the entire generation image; and identifier generating means for generating the identifier using the total feature quantity of the generation image and a true label indicating whether or not the generation image is a positive image or a negative image; wherein the feature-point feature-quantity extracting means divides a feature-point area into a plurality of small areas by separating the feature-point area as an area having a feature point as a center in an angular direction and a distance direction on the basis of the feature point in each of a plurality of response images obtained by filtering the generation image by a plurality of filters having different characteristics, produces a statistical quantity of pixel values of a small area for each of the plurality of small areas, and sets the statistical quantity of each of the plurality of small areas obtained from each of the plurality of response images for the feature point as a feature-point feature-quantity of the feature point, and wherein the identifier generating means generates the identifier for performing identification using a dimensional feature quantity to decrease an error value representing an identification error level of the positive and negative images among a plurality of dimensional feature quantities which are components of the multi-dimensional vector as the total feature quantity, and generates dimensional information representing a dimension of the dimensional feature quantity to decrease the error value.