# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 2 Literature Review  
[PERSON] et al. [4] proposed the use of a histogram of sign and magnitude extracted using binary gradient contour (BGC) for finger vein recognition. The region of interest (ROI) of the finger was normalized, cropped, resized, and processed with adaptive histogram equalization. BGC is then used to extract the sign and magnitude from the image. The image was divided into a set of cells. From each cell, the histogram of sign and magnitude were found. The histograms were then concatenated with each other to form the final feature representation. The lowest EERs of the proposed method using the SDUMLA-HMT finger vein database for the right index finger and left index finger was 0.353% and 0.390%, respectively. [PERSON] and [PERSON] [5] proposed a score-level fusion of finger vein and fingerprint biometrics. The orientation field of the fingerprint was first calculated. This was followed by image enhancement, fingerprint thinning, minutiae extraction, and minutiae matching based on ternary vector with alignment. In addition, the finger vein image was normalized in size and grayscale, and enhanced using a ridge wave filter. Afterward, segmentation and thinning of the finger vein were carried out. Finger vein matching was performed by using modified Hausdorff distance (MHD). For fusion, the finger vein matching distance was normalized using min-max normalization; while the fusion of finger vein and fingerprint score was performed using a weighted sum. The proposed method reported a recognition rate of 98.74% using a local database of 2880 finger veins and fingerprint images from 80 fingers. [PERSON] and [PERSON] [6] combined finger vein and fingerprint biometrics at the feature level. The fingerprint ROI was extracted using orientation and then cropped into 168 \(\times\) 168 pixels in size. On the other hand, the finger vein ROI was extracted by using the interphalangeal joint prior and cropped into 160 \(\times\) 80 pixels. Even symmetric Gabor filter (EGF) was applied to extract the features from the fingerprint and finger vein ROIs. The eight filtered images from the 2D convolution of the ROI and eight orientations of EGF were used to create a feature matrix. The average absolute deviation (AAD) of each 8 \(\times\) 8 or 16 \(\times\) 8 block were used to create the feature matrix of a filtered fingerprint or finger vein image. The feature matrix was then rearranged by row into a one-dimensional feature vector for each fingerprint and finger vein, respectively. It is then followed by the use of principal component analysis (PCA) for dimension reduction. Feature level fusion was then implemented using supervised local-preserving canonical correlation analysis (SLPCCA). to identify non-linear relationships between two sets of feature variables while preserving the local structure information in original data, exploiting the mutual information between two different feature sets, and exploiting the class information of the samples. SLPCCA generated a union vector called fingerprint-vein feature vector (FPVFV). The recognition rate of the proposed method is 99.687% using a local database of 640 fingerprints and finger veins from 64 individuals with 10 samples. [PERSON] et al. [7] proposed another feature-level fusion of finger vein and fingerprint biometrics. The fingerprint image was first normalized, followed by Gabor filter processing, binarization, thinning, minutiae extraction, neighborhood elimination, and ROI extraction. For finger vein biometrics, the vein was extracted by detecting the concavity in the grayscale image, followed by filtering, thinning, de-burring, minutiae extraction, neighborhood elimination, and ROI extraction. The feature-level fusion was implemented with feature concatenation. Neighborhood elimination was used to discard features that were within a certain radius of other features.