Accurate Human Recognition by Score-Level and Feature-Level Fusion Using Palm–Phalanges Print

Abstract

Here, a highly precise human identification system is developed using a newly proposed biometric trait—palm–phalanges print (PPP). For PPP, NSIT database has been used which includes palm–phalanges. This database consists of anterior hand images of fifty individuals with ten samples each. To crop the region of interest from hand samples to get palm–phalanges, database is preprocessed. First, it has been shown that each finger phalange can be used as a biometric modality and give moderate/sufficient performance for low-accuracy system. For feature extraction, histograms of oriented gradients, GMF feature, mean and AAD methods have been used. To further enhance the performance, score-level and feature-level fusion strategies are applied and compared. Score-level fusion is performed using different fusion rules. Next for feature-level fusion, five methods are used: (1) simple concatenate, (2) PCA feature fusion, (3) linear discriminant analysis feature fusion, (4) fusion codes and (5) supervised local-preserving canonical correlation analysis method. Receiver operating characteristics (ROC), equal error rate, area under the curve of ROC and decidability index (d) are used to show the performance of the system qualitatively and quantitatively.