A robust alignment-based personal recognition using center inner Knuckle prints

Biometrie based identification systems have beer widely used due to their reliability. Inner Knuckle Print images contain unique and reliable features for human identification. fr this paper, we propose a personal identification method using the Center Inner Knuckle Prints. The proposed method uses the Neighboring Direction Indicator features along with a perfect alignment and enhancement preprocessing steps that boost the performance compared to state-of-the-art methods. The performance of different feature extraction methods has been investigated using Sfax-Miracle Database, which is composed of low resolution hand images captured by a contactless capture in a free environment to test the effect of alignment and enhancement The effect of prints' fusion at the score level has also been investigated for a multimodal identification system. The result show that the proposed method outperforms state-of-the-are methods considering both Equal Error Rate and Best Identification Rate.