7. An object verification method comprising: performing, by a processor, a plurality of different matching algorithms on a query image and generating a plurality of scores; normalizing each of the generated scores to be adaptive to the quality of the query image by using a different normalization parameter; estimating, performed by a processor having computing devie-executable instructions, weights of the normalized scores based on the respective matching algorithms applied; and fusing the normalized scores by respectively applying the estimated weights to the normalized scores, wherein the estimating of the weights comprises: calculating an equal error rate (EER) of each matching algorithm in order to evaluate performance of each matching algorithm; calculating a weight exponent using a mean value of the calculated EERs; calculating a weight of each matching algorithm using the EER of each matching algorithm and the weight exponent; and normalizing the weight of each matching algorithm, and the calculating of the weight exponent is performed using Ie=a*E(EERm)2+b*E(EER)+c, E(EER)=max(3+log EERm, 0) wherein IE indicates a weight exponent, EERm indicates a mean value of the respective EERs of the matching algorithms, and a, b and c indicate constants.
7. An object verification method comprising: performing, by a processor, a plurality of different matching algorithms on a query image and generating a plurality of scores; normalizing each of the generated scores to be adaptive to the quality of the query image by using a different normalization parameter; estimating, performed by a processor having computing devie-executable instructions, weights of the normalized scores based on the respective matching algorithms applied; and fusing the normalized scores by respectively applying the estimated weights to the normalized scores, wherein the estimating of the weights comprises: calculating an equal error rate (EER) of each matching algorithm in order to evaluate performance of each matching algorithm; calculating a weight exponent using a mean value of the calculated EERs; calculating a weight of each matching algorithm using the EER of each matching algorithm and the weight exponent; and normalizing the weight of each matching algorithm, and the calculating of the weight exponent is performed using Ie=a*E(EERm)2+b*E(EER)+c, E(EER)=max(3+log EERm, 0) wherein IE indicates a weight exponent, EERm indicates a mean value of the respective EERs of the matching algorithms, and a, b and c indicate constants. 13. The method of claim 7 , wherein the performing of the matching algorithms on the query image and the generating of the scores comprises: configuring R, G and B channel images from the query image; estimating a weight of each of the R, G and B channel images based on an EER and contribution of each of the R, G and B channel images; fusing the R, G and B channel images using the weights thereof and generating a grayscale image; and performing a matching algorithm on the grayscale image and generating a score of the query image.