Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF

Multimodal biometrics is an evolving technology in the fields of security. Biometrics system reduces the effort of remember a memorable password. Multimodal biometrics system uses two or more traits for efficient recognition. This paper presents a hand biometric system by fusing information of palmprint and finger knuckle. To this end, BSIF (Binarized Statistical Image Features) filter and LBP (Local binary patterns) coefficients are employed to obtain the Finger-knuckle-print and palm-print traits, and subsequently selection of the features vector is conducted with PCA (Principal Component Analysis) transforms in higher coefficients. To match the finger knuckle or palm-print feature vector, the (ELM) Extreme learning machine is applied. According to the experiment outcomes, the proposed system not only has a significantly high recognition rate but it also affords greater security compared to the single biometric system.