A biometric system using finger knuckle patterns has recently drawn the interest of researchers worldwide. Compared with its hand counterparts, palm and knuckle parts has several unique merits. Knuckle print based biometric system is highly acceptable because these patterns do not disturb with injury and wound on the finger surface. However, geometrical and textural features have been used widely in most of the existing knuckle print recognition methods where an individual is verified by extraction of size, length, position and shape features. This paper presents a matching score scheme for personal recognition using novel inner knuckle print-based features i.e., knuckle distance based geometrical features, and texture features. The proposed method is tested on our in-house Hand database (720 images) collected over a span of 3 months. Finally, the test and training images are compared in terms of using fusion and decision algorithm.