Biometric methods are among the safest and most secure solutions for identity recognition and verification. One of the biometric features with sufficient uniqueness for identity recognition is finger knuckle print (FKP). This paper presents a new method of identity recognition and verification based on FKP features, where feature extraction is combined with entropy-based pattern histogram and a set of statistical textural features. The genetic algorithm is then used to find the superior features among those extracted. After extracting superior features, a support vector machine-based feedback scheme is used to improve the performance of the biometric system. Two databases called Poly-U FKP and FKP are used for performance evaluation. The proposed method managed to achieve 94.91% and 98.5% recognition rate on Poly-U and FKP databases and outperformed all of the existing methods in this respect. These results demonstrate the potential of this method as a simple yet effective solution for FKP-based identity recognition.
