Fingerprint representations can be broadly divided into three categories: image level, texture features and minu- tiae features. Both image based and texture based repre- sentations require accurate alignment before comparison. This presents a problem since accurate registration of fin- gerprints is challenging. On the other hand, minutiae based matchers are invariant to changes in orientation and posi- tion, but completely ignore the rich visual content in the image. In this paper, we present a localized texture based representation scheme that relies solely on visual content for identification and at the same time does not require ab- solute alignment. We outline techniques to efficiently com- pute these features and also propose an algorithm to per- form identification based on these features. Our experimen- tal evaluations over database of several sizes show that the proposed features are both accurate and scalable.