An Improved Method Based on Weighted Grid Micro-structure Feature for Text-Independent Writer Recognition

Writer recognition is a very important branch of biometrics. In our previous research, a Grid Micro-structure Feature (GMSF) based text-independent and script-independent method was adopted and high performance was obtained. However, this method is sensitive to pen-width variation in practical situation. To solve this problem, an inner and inter class variances weighted high-dimensional feature matching method is proposed. The inner and inter class variances are estimated on handwriting samples with different pen-width written by different writers. Experimental results show that our method is effective.