Abstract

This paper proposes a new local image descriptor for Finger Knuckle Print Recognition Systems (FKPRS), named Log–Gabor Binarized Statistical Image Features descriptor (LGBSIF). The idea of LGBSIF is based on the image Log-Gabor wavelet representation and the Binarized Statistical Image Features (BSIF). Initially, the Region of Interest (ROI) of the FKP images are analyzed with a 1D Log-Gabor wavelet to extract the preliminary features that are presented by both the real and imaginary parts of the filtered image. The main motive of the LGBSIF is to enhance the Log-Gabor real and imaginary features by applying the BSIF coding method. Secondly, histograms extracted from the encoded real and imaginary images respectively are concatenated in one large feature vector. Thirdly, the PCA+LDA technique is used to reduce the dimensionality of this feature and enhance its discriminatory power. Finally, the Nearest Neighbor Classifier that uses the Cosine distance is employed for the matching process. The evaluation of the performance of the proposed system is done on the Poly-U FKP database. However, the experimental results have shown that the proposed system achieves better results than other state-of-the-art systems and confirmed the tenacity of the proposed descriptor. Further, the results also prove that the performance efficiency of the introduced system in terms of recognition rate (Rank1) and equal error rate (EER) are 100% and 0% for both modes of identification and verification respectively.