Genetic And Evolutionary Biometrics:Multiobjective, Multimodal, Feature Selection/Weighting For Tightly Coupled Periocular And Face Recognition

The Genetic & Evolutionary Computation (GEC) research community has seen the emergence of a new subarea, referred to as Genetic & Evolutionary Biometrics (GEB), as GECs have been applied to solve a variety of biometric problems. In this dissertation, we present three new GEB techniques for multibiometric recognition: Genetic & Evolutionary Feature Selection (GEFeS), Weighting (GEFeW), and Weighting/Selection (GEFeWS). Instead of selecting the most salient individual features, these techniques evolve subsets of the most salient combinations of features and/or weight features based on their discriminative ability in an effort to increase accuracy while decreasing the overall number of features needed for recognition. We also incorporate cross validation into our best performing technique in an attempt to evolve feature masks (FMs) that also generalize well to unseen subjects and we search the value preference space in an attempt to analyze its impact in respect to optimization and generalization. Our results show that by fusing the periocular biometric with the face, we can achieve higher recognition accuracies than using the two biometric modalities independently. Our results also show that our GEB techniques are able to achieve higher recognition rates than the baseline methods, while using significantly fewer features. In addition, by incorporating machine learning, we were able to create FMs that also generalize well to unseen subjects and use less than 50% of the extracted features. Finally, by searching the value preference space, we were able to determine which weights were most effective in terms of optimization and generalization

Genetic & Evolutionary Biometric Security: Disposable Feature Extractors for Mitigating Biometric Replay Attacks

AbstractBiometric-based access control systems (BACSs) are vulnerable to replay attacks. Replay attacks occur when a biometric template is intercepted and maliciously used to gain unauthorized access to a system. In this paper, we introduce a Genetic and Evolutionary Biometric Security (GEBS) application which uses a Genetic and Evolutionary Computation to develop disposable Feature Extractors (FEs) in an effort to mitigate replay attacks. We describe how a previously developed system known as GEFE (Genetic and Evolutionary Feature Extraction) can be used to evolve unique and disposable FEs for users of BACS. Furthermore, we propose two access control protocols based on the use of disposable FEs and/or their resulting templates (also referred to as feature vectors (FVs)). In our proposed protocols, FEs/FVs are used to authenticate the identity of individuals and are then discarded. Our results show that this GEBS application can be successfully used to mitigate biometric replay attacks