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 And Evolutionary Feature Selection And Weighting For Face Recognition

Investigated the hybridization of genetic-based feature selection (GEFeS), genetic-based feature weighting (GEFeW) and LBP-based face recognition techniques. The results indicate that feature selection and weighting enhances the overall performance of LBP-based face recognition techniques

A niching memetic algorithm for simultaneous clustering and feature selection

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data

Fast computation of the performance evaluation of biometric systems: application to multibiometric

The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.Comment: Future Generation Computer Systems (2012

Pattern Recognition of Surgically Altered Face Images Using Multi-Objective Evolutionary Algorithm

Plastic surgery has been recently coming up with a new and important aspect of face recognition alongside pose, expression, illumination, aging and disguise. Plastic surgery procedures changes the texture, appearance and the shape of different facial regions. Therefore, it is difficult for conventional face recognition algorithms to match a post-surgery face image with a pre-surgery face image. The non-linear variations produced by plastic surgery procedures are hard to be addressed using current face recognition algorithms. The multi-objective evolutionary algorithm is a novel approach for pattern recognition of surgically altered face images. The algorithms starts with generating non-disjoint face granules and two feature extractors EUCLBP (Extended Uniform Circular Local Binary Pattern) and SIFT (Scale Invariant Feature Transform), are used to extract discriminating facial information from face granules. DOI: 10.17762/ijritcc2321-8169.150316