Face, Fingerprint, and Signature based Multimodal Biometric System using Score Level and Decision Level Fusion Approaches

Abstract

Principal Component Analysis (PCA) is the best face recognition method. This research suggests PCA for fingerprint and signature recognition. Simple image processing transforms like DCT, 2D-DCT, DWT, SWT, 2D-SWT, SVD (Singular Vector Decomposition), Entropy, and Rank can be used for feature extraction. These transforms and measures are utilized with PCA as a feature extraction module to construct uni-modal and multimodal biometric systems using face, fingerprint, and signature modalities. Most PCA biometrics systems compare stored template to claimed identification using Euclidean distance. This paper proposes matching modules using similarity and dissimilarity measures viz. Absolute Pearson's Correlation Coefficient (APCC), Absolute Uncentered Pearson's Correlation Coefficient (AUPCC), Bray Curtis Distance (BC), Canberra distance (CB), Chebyshev Distance (CBS), Chessboard Distance (CSB), City block or Manhattan distance (CTB), Cross Correlation (CC), Dot product (DP), Euclidean distance (EUC), Extended Jaccard Distance (EJ), Hamming Distance (HM), Harmonically Summed Euclidean distance (HSEUC), Kendall Correlation Coefficient (KCC), Mahalanobis Distance (MH), Minimum Coordinate Difference (MCD), Minkowiski distance (MNK), Multivariate Kurtosis Coefficient (MVK), Multivariate Skew (MVS), Normalized City Block or Manhattan distance (NCTB), Normalized Cross-correlation (NCC), Normalized Euclidean distance (NEUC), Pearson’s Cosine Distance (PCOS), Pearson's Correlation Coefficient (PCC), Pearson's Absolute Value Dissimilarity (PAVD), Pearson's Linear Dissimilarity (PLDISS), Spearman Correlation Coefficient (SCC), Standardized Euclidean Distance (SEUC), Uncentered Pearson's Correlation Coefficient (UPCC), Wave-Hedges Distance (WVH). This study again discusses score level fusion of face, fingerprint, and signature using sum and max rules, z-score normalization, and decision level fusion using AND rule.