Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Analyse d’images de documents patrimoniaux : une approche structurelle à base de texture

Over the last few years, there has been tremendous growth in digitizing collections of cultural heritage documents. Thus, many challenges and open issues have been raised, such as information retrieval in digital libraries or analyzing page content of historical books. Recently, an important need has emerged which consists in designing a computer-aided characterization and categorization tool, able to index or group historical digitized book pages according to several criteria, mainly the layout structure and/or typographic/graphical characteristics of the historical document image content. Thus, the work conducted in this thesis presents an automatic approach for characterization and categorization of historical book pages. The proposed approach is applicable to a large variety of ancient books. In addition, it does not assume a priori knowledge regarding document image layout and content. It is based on the use of texture and graph algorithms to provide a rich and holistic description of the layout and content of the analyzed book pages to characterize and categorize historical book pages. The categorization is based on the characterization of the digitized page content by texture, shape, geometric and topological descriptors. This characterization is represented by a structural signature. More precisely, the signature-based characterization approach consists of two main stages. The first stage is extracting homogeneous regions. Then, the second one is proposing a graph-based page signature which is based on the extracted homogeneous regions, reflecting its layout and content. Afterwards, by comparing the different obtained graph-based signatures using a graph-matching paradigm, the similarities of digitized historical book page layout and/or content can be deduced. Subsequently, book pages with similar layout and/or content can be categorized and grouped, and a table of contents/summary of the analyzed digitized historical book can be provided automatically. As a consequence, numerous signature-based applications (e.g. information retrieval in digital libraries according to several criteria, page categorization) can be implemented for managing effectively a corpus or collections of books. To illustrate the effectiveness of the proposed page signature, a detailed experimental evaluation has been conducted in this work for assessing two possible categorization applications, unsupervised page classification and page stream segmentation. In addition, the different steps of the proposed approach have been evaluated on a large variety of historical document images.Les récents progrès dans la numérisation des collections de documents patrimoniaux ont ravivé de nouveaux défis afin de garantir une conservation durable et de fournir un accès plus large aux documents anciens. En parallèle de la recherche d'information dans les bibliothèques numériques ou l'analyse du contenu des pages numérisées dans les ouvrages anciens, la caractérisation et la catégorisation des pages d'ouvrages anciens a connu récemment un regain d'intérêt. Les efforts se concentrent autant sur le développement d'outils rapides et automatiques de caractérisation et catégorisation des pages d'ouvrages anciens, capables de classer les pages d'un ouvrage numérisé en fonction de plusieurs critères, notamment la structure des mises en page et/ou les caractéristiques typographiques/graphiques du contenu de ces pages. Ainsi, dans le cadre de cette thèse, nous proposons une approche permettant la caractérisation et la catégorisation automatiques des pages d'un ouvrage ancien. L'approche proposée se veut indépendante de la structure et du contenu de l'ouvrage analysé. Le principal avantage de ce travail réside dans le fait que l'approche s'affranchit des connaissances préalables, que ce soit concernant le contenu du document ou sa structure. Elle est basée sur une analyse des descripteurs de texture et une représentation structurelle en graphe afin de fournir une description riche permettant une catégorisation à partir du contenu graphique (capturé par la texture) et des mises en page (représentées par des graphes). En effet, cette catégorisation s'appuie sur la caractérisation du contenu de la page numérisée à l'aide d'une analyse des descripteurs de texture, de forme, géométriques et topologiques. Cette caractérisation est définie à l'aide d'une représentation structurelle. Dans le détail, l'approche de catégorisation se décompose en deux étapes principales successives. La première consiste à extraire des régions homogènes. La seconde vise à proposer une signature structurelle à base de texture, sous la forme d'un graphe, construite à partir des régions homogènes extraites et reflétant la structure de la page analysée. Cette signature assure la mise en œuvre de nombreuses applications pour gérer efficacement un corpus ou des collections de livres patrimoniaux (par exemple, la recherche d'information dans les bibliothèques numériques en fonction de plusieurs critères, ou la catégorisation des pages d'un même ouvrage). En comparant les différentes signatures structurelles par le biais de la distance d'édition entre graphes, les similitudes entre les pages d'un même ouvrage en termes de leurs mises en page et/ou contenus peuvent être déduites. Ainsi de suite, les pages ayant des mises en page et/ou contenus similaires peuvent être catégorisées, et un résumé/une table des matières de l'ouvrage analysé peut être alors généré automatiquement. Pour illustrer l'efficacité de la signature proposée, une étude expérimentale détaillée a été menée dans ce travail pour évaluer deux applications possibles de catégorisation de pages d'un même ouvrage, la classification non supervisée de pages et la segmentation de flux de pages d'un même ouvrage. En outre, les différentes étapes de l'approche proposée ont donné lieu à des évaluations par le biais d'expérimentations menées sur un large corpus de documents patrimoniaux

Out-of-plane action unit recognition using recurrent neural networks

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.The face is a fundamental tool to assist in interpersonal communication and interaction between people. Humans use facial expressions to consciously or subconsciously express their emotional states, such as anger or surprise. As humans, we are able to easily identify changes in facial expressions even in complicated scenarios, but the task of facial expression recognition and analysis is complex and challenging to a computer. The automatic analysis of facial expressions by computers has applications in several scientific subjects such as psychology, neurology, pain assessment, lie detection, intelligent environments, psychiatry, and emotion and paralinguistic communication. We look at methods of facial expression recognition, and in particular, the recognition of Facial Action Coding System’s (FACS) Action Units (AUs). Movements of individual muscles on the face are encoded by FACS from slightly different, instant changes in facial appearance. Contractions of specific facial muscles are related to a set of units called AUs. We make use of Speeded Up Robust Features (SURF) to extract keypoints from the face and use the SURF descriptors to create feature vectors. SURF provides smaller sized feature vectors than other commonly used feature extraction techniques. SURF is comparable to or outperforms other methods with respect to distinctiveness, robustness, and repeatability. It is also much faster than other feature detectors and descriptors. The SURF descriptor is scale and rotation invariant and is unaffected by small viewpoint changes or illumination changes. We use the SURF feature vectors to train a recurrent neural network (RNN) to recognize AUs from the Cohn-Kanade database. An RNN is able to handle temporal data received from image sequences in which an AU or combination of AUs are shown to develop from a neutral face. We are recognizing AUs as they provide a more fine-grained means of measurement that is independent of age, ethnicity, gender and different expression appearance. In addition to recognizing FACS AUs from the Cohn-Kanade database, we use our trained RNNs to recognize the development of pain in human subjects. We make use of the UNBC-McMaster pain database which contains image sequences of people experiencing pain. In some cases, the pain results in their face moving out-of-plane or some degree of in-plane movement. The temporal processing ability of RNNs can assist in classifying AUs where the face is occluded and not facing frontally for some part of the sequence. Results are promising when tested on the Cohn-Kanade database. We see higher overall recognition rates for upper face AUs than lower face AUs. Since keypoints are globally extracted from the face in our system, local feature extraction could provide improved recognition results in future work. We also see satisfactory recognition results when tested on samples with out-of-plane head movement, showing the temporal processing ability of RNNs

Content-based image retrieval of museum images

Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections

Multispectral scleral patterns for ocular biometric recognition

Biometrics is the science of recognizing people based on their physical or behavioral traits such as face, fingerprints, iris, and voice. Among the various traits studied in the literature, ocular biometrics has gained popularity due to the significant progress made in iris recognition. However, iris recognition is unfavorably influenced by the non-frontal gaze direction of the eye with respect to the acquisition device. In such scenarios, additional parts of the eye, such as the sclera (the white of the eye) may be of significance. In this dissertation, we investigate the use of the sclera texture and the vasculature patterns evident in the sclera as potential biometric cues. Iris patterns are better discerned in the near infrared spectrum (NIR) while vasculature patterns are better discerned in the visible spectrum (RGB). Therefore, multispectral images of the eye, consisting of both NIR and RGB channels, were used in this work in order to ensure that both the iris and the vasculature patterns are successfully imaged.;The contributions of this work include the following. Firstly, a multispectral ocular database was assembled by collecting high-resolution color infrared images of the left and right eyes of 103 subjects using the DuncanTech MS 3100 multispectral camera. Secondly, a novel segmentation algorithm was designed to localize the spacial extent of the iris, sclera and pupil in the ocular images. The proposed segmentation algorithm is a combination of region-based and edge-based schemes that exploits the multispectral information. Thirdly, different feature extraction and matching method were used to determine the potential of utilizing the sclera and the accompanying vasculature pattern as biometric cues. The three specific matching methods considered in this work were keypoint-based matching, direct correlation matching, and minutiae matching based on blood vessel bifurcations. Fourthly, the potential of designing a bimodal ocular system that combines the sclera biometric with the iris biometric was explored.;Experiments convey the efficacy of the proposed segmentation algorithm in localizing the sclera and the iris. The use of keypoint-based matching was observed to result in the best recognition performance for the scleral patterns. Finally, the possibility of utilizing the scleral patterns in conjunction with the iris for recognizing ocular images exhibiting non-frontal gaze directions was established

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy