Marina L. Gavrilova

Multimodal Biometric System Using Rank-Level Fusion Approach

In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fishers linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.

Multimodal Biometrics and Intelligent Image Processing for Security Systems

Although it is a relatively new approach to biometric knowledge representation, multimodal biometric systems have emerged as an innovative alternative that aids in developing a more reliable and efficient security system.Multimodal Biometrics and Intelligent Image Processing for Security Systems provides an in-depth description of existing and fresh fusion approaches for multimodal biometric systems. Covering relevant topics affecting the security and intelligent industries, this reference will be useful for readers from both academia and industry in the areas of pattern recognition, security, and image processing domains.

Artimetrics: Biometrics for Artificial Entities

Domestic and industrial robots, intelligent software agents, virtual-world avatars, and other artificial entities are being created and deployed in our society for various routine and hazardous tasks, as well as for entertainment and companionship. Over the past ten years or so, primarily in response to the growing security threats and financial fraud, it has become necessary to accurately authenticate the identities of human beings using biometrics. For similar reasons, it may become essential to determine the identities of nonbiological entities. Trust and security issues associated with the large-scale deployment of military soldier-robots [55], robot museum guides [22], software office assistants [24], human like biped robots [67], office robots [5], domestic and industrial androids [93], [76], bots [85], robots with humanlike faces [60], virtual-world avatars [109], and thousands of other man-made entities require the development of methods for a decentralized, affordable, automatic, fast, secure, reliable, and accurate means of authenticating these artificial agents. The approach has to be decentralized to allow authority-free authentication important for open-source and collaborative societies. To address these concerns, we proposed [117], [120], [119], [38] the concept of artimetricsa field of study that identifies, classifies, and authenticates robots, software, and virtual reality agents. In this article, unless otherwise qualified, the term robot refers to both embodied robots (industrial, mobile, tele, personal, military, and service) and virtual robots or avatars, focusing specifically on those that have a human morphology.

Gauss–Laguerre wavelet textural feature fusion with geometrical information for facial expression identification

Facial expressions are a valuable source of information that accompanies facial biometrics. Early detection of physiological and psycho-emotional data from facial expressions is linked to the situational awareness module of any advanced biometric system for personal state re/identification. In this article, a new method that utilizes both texture and geometric information of facial fiducial points is presented. We investigate Gauss–Laguerre wavelets, which have rich frequency extraction capabilities, to extract texture information of various facial expressions. Rotation invariance and the multiscale approach of these wavelets make the feature extraction robust. Moreover, geometric positions of fiducial points provide valuable information for upper/lower face action units. The combination of these two types of features is used for facial expression classification. The performance of this system has been validated on three public databases: the JAFFE, the Cohn-Kanade, and the MMI image.

FACIAL METAMORPHOSIS USING GEOMETRICAL METHODS FOR BIOMETRIC APPLICATIONS

Facial expression modeling has been a popular topic in biometrics for many years. One of the emerging recent trends is capturing subtle details such as wrinkles, creases and minor imperfections that are highly important for biometric modeling as well as matching. In this paper, we suggest a novel approach to the problem of expression modeling and morphing based on a geometry-based paradigm. In 2D image space, a distance-based morphing system is utilized to create a line drawing style facial animation from two input images representing frontal and profile views of the face. Aging wrinkles and expression lines are extracted and mapped back to the synthesized facial NPR (nonphotorealistic) sketches. In 3D object space, we present a metamorphosis system that combines the traditional free-form deformation (FFD) model with data interpolation techniques based on the proximity preserving Voronoi diagram. With feature points selected from two images of the target face, the proposed system generates the 3D target facial model by transforming a generic model. Experimental results demonstrate that morphing sequences generated by our systems are of convincing quality.

Multimodal Cancelable Biometrics

Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains. It has to do with actual biometric characteristics of users being permanent, and their number being limited. Thus, if users biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. Cancellable biometric for individual biometric has been a significantly understudied problem. The concept of cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that users can change their single biometric template in a biometric security system. However, cancelability in multimodal biometric has been barely addressed at all. In this paper, we tackle the problem and present a novel solution for cancelable biometrics in multimodal system. We develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on multi-modal face and ear database.

Markov chain model for multimodal biometric rank fusion

Multimodal biometric aims at increasing reliability of biometric systems through utilizing more than one biometric in decision-making process. An effective fusion scheme is necessary for combining information from various sources. Such information can be integrated at several distinct levels, such as sensor level, feature level, match score level, rank level, and decision level. In this paper, we present a multimodal biometric system utilizing face, iris, and ear biometric features through rank level fusion method using novel Markov chain approach. We first apply fisherimage technique to face and ear image databases for recognition and Hough transform and Hamming distance techniques for iris image recognition. The main contribution is in introducing Markov chain approach for biometric rank aggregation. One of the distinctive features of this method is that it satisfies the Condorcet criterion, which is essential in any fair rank aggregation system. The experimentation shows superiority of the proposed approach to other recently introduced biometric rank aggregation methods. The developed system can be effectively used by security and intelligence services for controlling access to prohibited areas and protecting important national or public information.

Applying Biometric Principles to Avatar Recognition

Domestic and industrial robots, intelligent software agents, virtual world avatars and other artificial entities are quickly becoming a part of our everyday life. Just like it is necessary to accurately authenticate identity of human beings, it is becoming essential to be able to determine identities of non-biological agents. In this paper, we present the current state of the art in virtual reality security, focusing specifically on emerging methodologies for avatar authentication. We also outline future directions and potential applications for this high impact research field.

Decision Fusion for Multimodal Biometrics Using Social Network Analysis

This paper presents for the first time decision fusion for multimodal biometric system using social network analysis (SNA). The main challenge in the design of biometric systems, at present, lies in unavailability of high-quality data to ensure consistently high recognition results. Resorting to multimodal biometric partially solves the problem, however, issues with dimensionality reduction, classifier selection, and aggregated decision making remain. The presented methodology successfully overcomes the problem through employing novel decision fusion using SNA. While several types of feature extractors can be used to reduce the dimension and identify significant features, we chose the Fisher Linear Discriminant Analysis as one of the most efficient methods. Social networks are constructed based on similarity and correlation of features among the classes. The final classification result is generated based on the two levels of decision fusion methods. At the first level, individual biometrics (face or ear or signature) are classified using matching score methodology. SNA is used to reinforce the confidence level of the classifier to reduce the error rate. In the second level, outcomes of classification based on individual biometrics are fused together to obtain the final decision.

Facial biometrics for situational awareness systems

This study contributes to developing the concept of decision-making support in biometric-based situational awareness systems. Such systems assist users in gathering and analysing biometric data, and support the decision-making on the human behavioural pattern and/or authentication. As an example, the authors consider a facial biometric assistant that functions based on multi-spectral biometrics in visible and infrared bands; it involves facial expression recognition, face recognition in both spectra, as well as estimation of physiological parameters. The authors also investigate usage of facial biometrics for the semantic representation for advanced decision-making.