1. A subject discriminating apparatus comprising: an image input means for receiving a target image, which is a target of discrimination; a characteristic amount calculating means for calculating at least one characteristic amount, which is employed in discriminating a predetermined subject that includes at least one structural component, from the target image; a first discriminating means for discriminating whether the predetermined subject is included in the target image, by referring to first reference data in which the at least one characteristic amount and discrimination conditions corresponding to the at least one characteristic amount are defined in advance, with the at least one characteristic amount calculated from the target image while the target image is deformed in a stepwise manner within a variance corresponding to a first predetermined allowable range, wherein the first reference data is obtained by learning the at least one characteristic amount within a plurality of sample images, in which at least one of the positions and the positional relationships of the at least one structural component are normalized within the first predetermined allowable range, and a plurality of sample image groups, which are known to not include the predetermined subject, with a machine learning technique; and a second discriminating means for discriminating the positions of the at least one structural component included in the target image, by referring to second reference data in which the at least one characteristic amount and discrimination conditions corresponding to the at least one characteristic amount are defined in advance, with the at least one characteristic amount calculated from the target image while the target image is deformed in a stepwise manner within a variance corresponding to a second predetermined allowable range which is smaller than the first allowable range in the case that the first discriminating means judges that the predetermined subject is included in the target image, wherein the second reference data is obtained by learning the at least one characteristic amount within a plurality of sample images, in which at least one of the positions and the positional relationships of the at least one structural component are normalized within the second predetermined allowable range, and a plurality of sample image groups, which are known to not include the predetermined subject, with a machine learning technique.