An efficient personal identification by using text-independent writer recognition

Writer recognition, as an identification technology, has many advantages, such as natural interaction and non-intrusive detection, thus it becomes a hot topic in pattern recognition and machine learning research area. This paper proposes a new writer recognition algorithm of text independent, which adopts Independent Component Analysis (ICA) to extract texture feature and competitive learning mechanism to determine the center of class. Experimental results show that our algorithm is efficient.