Improving the Performance of Finger Vein Recognition Using the Local Histogram Concatenation of Image Descriptors

Abstract

In this paper, a system based on image descriptor and Local Histogram Concatenation (LHC) for finger vein recognition is introduced. The LHC of image descriptors such as LBP, LDP CLBP cannot be inverted back to the original images, therefore they can provide good security if stored as enrolled data. On the other hand, the technique of LHC does not depict spatial information, therefore it is expected to be less sensitive to image misalignment if a measure such as the histogram difference [Formula: see text] is used for recognition. The use of histogram difference makes the system more robust to misalignment compared to the pixel-by-pixel-based measures such as the Hamming Distance (HD). The approach of LHC is implemented by dividing the image descriptor into non-overlapped grids, then the histogram within each grid is calculated and concatenated with the histograms of the preceding grids and finally, the concatenated histograms of each two images are compared using [Formula: see text] measure. Two datasets, UTFVP and SDUMLA-HMT, are used for testing the performance of the system. The results have shown that the Identification Recognition Rate (IRR) is improved when LHCs of the image descriptors with [Formula: see text] measure are used compared to the use of only the image descriptors with HD measure. For UTFVP dataset, the IRR values were 97.44%, 95% and 98.37% when LHC and [Formula: see text] were used with LBP, LDP and CLBP, respectively, while these values were 89.44%, 92.63% and 92.92% when only LBP, LDP and CLBP with HD were used. For SDUMLA-HMT dataset, the IRR values of the system were 98.43%, 98.69% and 98.85% when LHC and [Formula: see text] were used with LBP, LDP and CLBP, respectively, while these values were 97.6%, 98.24% and 97.27% when only the image descriptors LBP, LDP and CLBP with HD were used.