Abstract
Writer identification is an important and active branch of biometrics, which means the methods for uniquely recognizing humans based upon their intrinsic physical or behavioral traits. In this paper, we propose one new method for off-line, text-independent writer identification by using the fractal dimension of wavelet subbands in Gabor domain of the handwriting images. In this method, the handwriting images are firstly decomposed into a series of Gabor subbands at different orientations and frequencies. Every Gabor subband is extended into one data sequence. Then, every sequence is decomposed into a series of wavelet subpatterns by wavelet transform. Afterwards, the mesh fractal dimensions of every wavelet subpattern are extracted as the feature for writer identification. Compared to the traditional Gabor method for off-line, text-independent writer identification, our method can extract more effective features to distinguish the handwritings, and hence achieve much better identification results.