Multi-instance Finger Knuckle Print Recognition based on Fusion of Local Features

Personal identity has become an important asset in today's digital world for any individual in society. Biometrics offers itself as a reliable and secure guarantor of our identities, so it has become essential to build efficient and robust recognition systems. In this orientation, we propose a fusion approach, which aims to optimally exploit the dividing block dimensions in the case of local methods to reduce similarities. We will use the compound local binary model (CLBP) for local features extraction, a robust operator descriptor that exploits both the sign and the inclination information of the differences between the center and the neighbor gray values. The reliability of the proposed approach was evaluated on the PolyU Finger Knuckle Print (FKP) database. We presented several experimental results that show the detailed path of our approach, explain the choices made for each step and illustrate the significant improvements compared to other existing recognition systems in the literature. The recognition rate of the proposed global approach is one of the highest among the other methods. Optimal final approach recognition rates vary between 99.70% and 100%.