1. An apparatus for detecting eyes, comprising: an eye candidate detector which divides an input face image into left and right images and detects at least one eye candidate from each of limited image regions of the left and right images by binarizing each of the limited image regions based on threshold values corresponding to each of the limited image regions; an eye candidate evaluator which evaluates the eye candidates by evaluating each combination of the eye candidates using geometric information as to the eye candidates to filter out eye candidates that cannot be eyes; a learning database which stores a plurality of face images in which positions of eyes are arranged and a plurality of face images which do not include eyes or in which positions of eyes are not arranged; and an eye candidate verifier which verifies the eye candidates with reference to the learning database and outputs an eye detection result signal, wherein the eye candidate verifier includes: a support vector machine (SVM) classifier which receives from the eye candidate evaluator the face image including the eye candidates and calculates an output value using an SVM algorithm; a maximum output value selector which selects a maximum output value among the calculated output values; and an eye determiner which, when the maximum output value is at least equal to a threshold value, determines that the detection of the eyes has succeeded, and when the maximum output value is less than the threshold value, determines that the detection of the eyes has failed, and wherein the SVM classifier includes: a first support vector machine sub-classifier which calculates an output value for a general face; a second support vector machine sub-classifier which calculates an output value for a face with eyeglasses; and a third support vector machine sub-classifier which calculates an output value for a face with long hair, wherein the eye candidate detector binarizes the input face image based on a first threshold value, extracts outlines from the limited image regions of the binarized face image, determines whether the outlines are satisfactory based on shapes and sizes of the extracted outlines and, when it is determined that the outlines are not satisfactory outlines, increasing the first threshold by a first value and repeating the binarization of the input face image based on the first threshold value and the extraction of the outlines from the limited image regions of the binarized face image; and detects eye candidates within the satisfactory outlines, and wherein before the eye candidates within the satisfactory outlines are detected, the detection of the at least one eye candidate from the limited image regions of the left and right images includes, when a number of satisfactory outlines of the extracted outlines is less than a value M, increasing the first threshold value by a second value less than the first value and repeating the binarization of the input face image based on the first threshold value and the extraction of the outlines from the limited image regions of the binarized face image.