Michael C. Fairhurst

Gabor wavelets and General Discriminant Analysis for face identification and verification

A novel and uniform framework for both face identification and verification is presented in this paper. The framework is based on a combination of Gabor wavelets and General Discriminant Analysis, and can be considered appearance based in that features are extracted from the whole face image. The feature vectors are then subjected to subspace projection. The design of Gabor filters for facial feature extraction is also discussed, which is seldom reported in the literature. The method has been tested extensively for both identification and verification applications. The FERET and BANCA face databases were used to generate the results. Experiments show that Gabor wavelets can significantly improve system performance whilst General Discriminant Analysis outperforms other subspace projection methods such as Principal Component Analysis, Linear Discriminant Analysis, and Kernel Principal Component Analysis. Our method has achieved 97.5% recognition rate on the FERET database, and 5.96% verification error rate on the BANCA database. This is a significantly better performance than that attainable with other popular approaches reported in the literature. In particular, our verification system performed better than most of the systems in the 2004 International Face Verification Competition, using the BANCA face database and specially designed test protocols.

The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1 over the Internet, 2 in an office environment with desktop PC, and 3 in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

Multiple classifier decision combination strategies for character recognition: A review

Abstract.Two research strands, each identifying an area of markedly increasing importance in the current development of pattern analysis technology, underlie the review covered by this paper, and are drawn together to offer both a task-oriented and a fundamentally generic perspective on the discipline of pattern recognition. The first of these is the concept of decision fusion for high-performance pattern recognition, where (often very diverse) classification technologies, each providing complementary sources of information about class membership, can be integrated to provide more accurate, robust and reliable classification decisions. The second is the rapid expansion in technology for the automated analysis of (especially) handwritten data for OCR applications including document and form processing, pen-based computing, forensic analysis, biometrics and security, and many other areas, especially those which seek to provide online or offline processing of data which is available in a human-oriented medium. Classifier combination/multiple expert processing has a long history, but the sheer volume and diversity of possible strategies now available suggest that it is timely to consider a structured review of the field. Handwritten character processing provides an ideal context for such a review, both allowing engagement with a problem area which lends itself ideally to the performance enhancements offered by multi-classifier configurations, but also allowing a clearer focus to what otherwise, because of the unlimited application horizons, would be a task of unmanageable proportions. Hence, this paper explicitly reviews the field of multiple classifier decision combination strategies for character recognition, from some of its early roots to the present day. In order to give structure and a sense of direction to the review, a new taxonomy for categorising approaches is defined and explored, and this both imposes a discipline on the presentation of the material available and helps to clarify the mechanisms by which multi-classifier configurations deliver performance enhancements. The review incorporates a discussion both of processing structures themselves and a range of important related topics which are essential to maximise an understanding of the potential of such structures. Most importantly, the paper illustrates explicitly how the principles underlying the application of multi-classifier approaches to character recognition can easily generalise to a wide variety of different task domains.

Recognition of handwritten Bengali characters: a novel multistage approach

A multistage scheme for the recognition of handwritten Bengali characters is introduced. An analysis of the Bengali character set has been carried out to isolate specific high-level features that can help in forming smaller sub-groups within the character set. This analysis demonstrates how detection of these various high-level features might help formulate successful multistage OCR design. A multiple expert decision combination hierarchy has been exploited to achieve higher performance from the proposed multi-stage framework.

Robust Road Modeling and Tracking Using Condensation

In this paper, we present a robust road detection and tracking method based on a condensation particle filter for real-time video-based navigation applications. The image is divided into horizontal strips, and vanishing point (VP) detection is performed on each image strip. We propose a method for estimating the density of road boundary line segments in the image so that VP detection in an image strip takes into account the detection results in the neighboring image strips. This use of contextual information for VP detection leads to more accurate detection results. The estimated road parameters are then used to initialize the condensation tracker. Experiments using real road videos demonstrate the robustness of our method to difficult road conditions due to the presence of partial occlusion, shadows, and road signs.

Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variations

In recent years, strategies based on combination of multiple classifiers have created great interest in the character recognition research community. A huge number of complex and sophisticated decision combination strategies have been explored by researchers. However, it has been realized recently that the comparatively simple Majority Voting System and its variations can achieve very robust and often comparable, if not better, performance than many of these complex systems. In this paper, a review of various Majority Voting Systems and their variations are discussed, and a comparative study of some of these methods is presented for a typical character recognition task.

An evaluation of multi-expert configurations for the recognition of handwritten numerals

In recent years, combination of multiple experts has become a major area of interest in designing practical and robust handwritten character recognition systems. Instead of building a single sophisticated and complicated classifier which is capable to handling all the various types of variations that are present in the handwritten character set, it has proved more prudent to apply relatively simpler classifiers (experts) by formulating ways of combining their individual decisions in order to generate robust and confident decisions. This paper presents a new class of decision combination approaches and compares the effectiveness of these approaches in successfully combining decisions by multiple experts in the specific application of handwritten numeral recognition. Although the proposed approaches have been applied to a specific task of handwritten numeral recognition, the underlying concepts are completely generalised and should be applicable to a very broad task domain.

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.

Template-Free Biometric-Key Generation by Means of Fuzzy Genetic Clustering

Biometric authentication is increasingly gaining popularity in a wide range of applications. However, the storage of the biometric templates and/or encryption keys that are necessary for such applications is a matter of serious concern, as the compromise of templates or keys necessarily compromises the information secured by those keys. In this paper, we propose a novel method, which requires storage of neither biometric templates nor encryption keys, by directly generating the keys from statistical features of biometric data. An outline of the process is as follows: given biometric samples, a set of statistical features is first extracted from each sample. On each feature subset or single feature, we model the intra and interuser variation by clustering the data into natural clusters using a fuzzy genetic clustering algorithm. Based on the modelling results, we subsequently quantify the consistency of each feature subset or single feature for each user. By selecting the most consistent feature subsets and/or single features for each user individually, we generate the key reliably without compromising its relative security. The proposed method is evaluated on handwritten signature data and compared with related methods, and the results are very promising.

Serial Combination of Multiple Experts: A Unified Evaluation

Abstract: Multiple expert decision combination has received much attention in recent years. This is a multi-disciplinary branch of pattern recognition which has extensive applications in numerous fields including robotic vision, artificial intelligence, document processing, office automation, human-computer interfaces, data acquisition, storage and retrieval, etc. In recent years, this application area has been extended to forensic science, including the identification of individuals using measures depending on biometrics, security and other applications. In this paper, a generalised multi-expert multi-level decision combination strategy, the serial combination approach, has been investigated from the dual viewpoints of theoretical analysis and practical implementation. Different researchers have implicitly utilised various approaches based on this concept over the years in a wide spectrum of application domains, but a comprehensive, coherent and generalised presentation of this approach from both theoretical and implementation viewpoints has not been attempted. While presenting here a unified framework for serial multiple expert decision combination, it is shown that many multi-expert approaches reported in the literature can be easily represented within the proposed framework. Detailed theoretical and practical discussions of the various performance results with these combinations, analysis of the internal processing of this approach, a case study for testing the theoretical framework, issues relating to processing overheads associated with the implementation of this approach, general comments on its applicability to various task domains and the generality of the approach in terms of reevaluating previous research have also been incorporated.