# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 2 Literature Review
The features were categorized as excellent or poor-quality features based on five evaluation factors: the number of input minutiae, the size of effective area, the change in the number of minutiae before and after minutiae reduction, the proportion of the number of registered minutiae and input minutiae, and the degree of center deviation. Matching was performed by dynamic weighting matching in which the quality of the features was used to determine the weights of the weighted sum of the matching pairs of features. The proposed method reported a recognition rate of 98.93% using a local database with 40 pairs of images where each pair was composed of a finger vein sample and a fingerprint sample. [PERSON] et al. [8] adopted the reduction of high-scores effect (RHE) normalization for score level fusion of fingerprint, face, and finger vein biometrics. The normalization would only be done when the two feature sets have different ranges, in order to minimize information loss. The normalization was performed by using the sum of standard deviation and mean of the genuine distribution to replace the maximum value used in min-max normalization. The proposed normalization was tested using support vector machine (SVM)-based fusion and sum rule-based fusion. The sum rule-based fusion was done by summing all the scores from the matches and when the summed score is above a threshold, it is matched. The SVM-based fusion used C-support vector classification (C-SVC) where the scores from all matches are the feature vector. C-SVC refers to the support vector classifier that uses parameter C as a regularization parameter that implements a penalty on misclassifications, that ranges from zero to infinity. The GAR from the proposed system using sum rule-based fusion was 99.6% at 0.001% FAR using a local database with 510 individuals. The GAR from the proposed system using SVM with radial basis function (RBF) kernel was 99.9% at 0.0003% FAR using a similar dataset. [PERSON] et al. [9] performed a study exploring the fusion of fingerprint, finger-knuckle-print, and finger vein traits at feature level fusion and decision level fusion. The study made use of Gabor filter 2D to preprocess the three different types of biometric images. The feature extraction was done by using a bank of Gabor filters to obtain a single feature vector. Feature selection was then conducted for each feature vector by using kernel fisher analysis (KFA). In the feature level fusion, the feature vector of three different biometric traits was simply concatenated and then classified by SVM and k-nearest neighbors (KNN). In the enhanced feature level fusion, the feature vectors were concatenated and this was followed by feature selection using KFA. After that, the selected concatenated feature vector was classified by SVM and KNN. Additionally, decision level fusion of the classification results was performed by using majority voting. Two publicly available datasets, the SDUMA-HMT finger vein database, and the Poly U dataset were used for performance evaluation. The best results for the proposed system using SVM with feature level fusion enhanced feature level fusion, and decision level fusion were 99.96%, 99.69%, and 94.91%, respectively, while the results of KNN with the three fusion methods were 88.21%, 99.06%, and 95.28%, respectively. [PERSON] et al. [10] proposed a multimodal biometric system by using finger vein and finger shape biometrics for personal recognition. The finger image is first segmented by using Sobel filtering, component labeling, thresholding, and convex hull processing to determine the finger ROI. The finger is then normalized by rotating and cropping. For finger shape recognition, the finger is transformed into a two-dimensional spectrogram image by calculating the frequency of the finger thickness using a short-time Fourier transform (STFT) by sliding a window along the length of the finger. The spectrogram image is then used in a convolutional neural network (CNN) to extract the finger shape feature vector.