A kind of correlation classification distance of whole phase based on weight
Liu Yang, He Yan, Yue Xue Dong, Liu Ying Fei · 2010
In the process of iris classification, a new classification distance with adjustable weight which takes advantage of whole phase information to encode is proposed. The method is to use feature extraction function to do the extraction toward all iris image, which could obtain real and imaginary part iris information. Then, tangent function is used to transform extracted real and imaginary part characteristics into phase information and adjust the phase angle to the range (-π + π). The eigenvector that contained all image phase angle information will be set up to do with the weighted inner product and normalization processing to obtain normalized classification distance between the iris samples. The experiment shows this algorithm could modify the 4-quadrant phase encoding problem that might omit details of the phase information, and the classification effect is encouraged.