A Multi-Biometric System Based on Feature and Score Level Fusions

In general, the information of multiple biometric modalities is fused at a single level, for example, score level or feature level. The recognition accuracy of a multimodal biometric system may not be improved by carrying fusion at a single level, since one matcher may provide a performance lower than that provided by other matchers. In view of this, we propose a new fusion scheme, referred to as the matcher performance-based (MPb) fusion scheme, in which the fusion is carried out at two levels, feature level, and score level, to improve the overall recognition accuracy. First, we consider the performance of the individual matchers in order to find out which of the modalities should be used for fusion at the feature level. Then, the selected modalities are fused at this level by utilizing their encoded features. Next, we fuse the score obtained from the feature-level fusion with that of the modality for which the performance is the highest. In order to carry out this fusion, a new normalization technique referred to as the overlap extrema-variation-based anchored min-max (OEVBAMM) normalization technique, is also proposed. By considering three modalities, namely, fingerprint, palmprint, and earprint, the performance of the proposed fusion scheme as well as that of the single level fusion scheme, both with various normalization and weighting techniques are evaluated in terms of a number of metrics. It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method

Fusion of face and iris biometrics in security verification systems.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students