Personal verification based on multi-spectral finger texture lighting images

Finger Texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the Surrounded Patterns Code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. The SPC approach proposes using a single texture descriptor for FT images captured under multispectral illuminations, where this reduces the cost of employing different feature extraction methods for different spectral FT images. Furthermore, a novel classifier termed the Re-enforced Probabilistic Neural Network (RPNN) is proposed. It enhances the capability of the standard Probabilistic Neural Network (PNN) and provides better recognition performance. Two types of FT images from the Multi-Spectral CASIA (MSCASIA) database were employed as two types of spectral sensors were used in the acquiring device: the White (WHT) light and spectral 460 nm of Blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the Equal Error Rates (EERs) at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilizing the RPNN

Signal processing and machine learning techniques for human verification based on finger textures

PhD ThesisIn recent years, Finger Textures (FTs) have attracted considerable attention as potential biometric characteristics. They can provide robust recognition performance as they have various human-speci c features, such as wrinkles and apparent lines distributed along the inner surface of all ngers. The main topic of this thesis is verifying people according to their unique FT patterns by exploiting signal processing and machine learning techniques. A Robust Finger Segmentation (RFS) method is rst proposed to isolate nger images from a hand area. It is able to detect the ngers as objects from a hand image. An e cient adaptive nger segmentation method is also suggested to address the problem of alignment variations in the hand image called the Adaptive and Robust Finger Segmentation (ARFS) method. A new Multi-scale Sobel Angles Local Binary Pattern (MSALBP) feature extraction method is proposed which combines the Sobel direction angles with the Multi-Scale Local Binary Pattern (MSLBP). Moreover, an enhanced method called the Enhanced Local Line Binary Pattern (ELLBP) is designed to e ciently analyse the FT patterns. As a result, a powerful human veri cation scheme based on nger Feature Level Fusion with a Probabilistic Neural Network (FLFPNN) is proposed. A multi-object fusion method, termed the Finger Contribution Fusion Neural Network (FCFNN), combines the contribution scores of the nger objects. The veri cation performances are examined in the case of missing FT areas. Consequently, to overcome nger regions which are poorly imaged a method is suggested to salvage missing FT elements by exploiting the information embedded within the trained Probabilistic Neural Network (PNN). Finally, a novel method to produce a Receiver Operating Characteristic (ROC) curve from a PNN is suggested. Furthermore, additional development to this method is applied to generate the ROC graph from the FCFNN. Three databases are employed for evaluation: The Hong Kong Polytechnic University Contact-free 3D/2D (PolyU3D2D), Indian Institute of Technology (IIT) Delhi and Spectral 460nm (S460) from the CASIA Multi-Spectral (CASIAMS) databases. Comparative simulation studies con rm the e ciency of the proposed methods for human veri cation. The main advantage of both segmentation approaches, the RFS and ARFS, is that they can collect all the FT features. The best results have been benchmarked for the ELLBP feature extraction with the FCFNN, where the best Equal Error Rate (EER) values for the three databases PolyU3D2D, IIT Delhi and CASIAMS (S460) have been achieved 0.11%, 1.35% and 0%, respectively. The proposed salvage approach for the missing feature elements has the capability to enhance the veri cation performance for the FLFPNN. Moreover, ROC graphs have been successively established from the PNN and FCFNN.the ministry of higher education and scientific research in Iraq (MOHESR); the Technical college of Mosul; the Iraqi Cultural Attach e; the active people in the MOHESR, who strongly supported Iraqi students

Challenges and Success Factors of Scaled Agile Adoption – A South African Perspective

Agile methods and Agile scaling frameworks have become a solution for software-developing organizations striving to improve the success of software projects. Agile methods were developed for small projects, but due to their benefits, even large software-developing organizations have adopted them to scale their software projects. This quantitative study was undertaken to deepen the researchers’ understanding of the critical success factors and challenges of Scaled Agile from the South African perspective. A simple random sampling method was used. Data was collected with the use of an online structured questionnaire and the response rate was 70%. The results reveal that customer satisfaction remains at the epicenter of adopting Scaled Agile methods. Lack of top management support remains the major challenge in adopting Scaled Agile. The results reveal some notable changes when it comes to the most adopted Agile scaling framework

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Feature extraction and information fusion in face and palmprint multimodal biometrics

ThesisMultimodal biometric systems that integrate the biometric traits from several modalities are able to overcome the limitations of single modal biometrics. Fusing the information at an earlier level by consolidating the features given by different traits can give a better result due to the richness of information at this stage. In this thesis, three novel methods are derived and implemented on face and palmprint modalities, taking advantage of the multimodal biometric fusion at feature level. The benefits of the proposed method are the enhanced capabilities in discriminating information in the fused features and capturing all of the information required to improve the classification performance. Multimodal biometric proposed here consists of several stages such as feature extraction, fusion, recognition and classification. Feature extraction gathers all important information from the raw images. A new local feature extraction method has been designed to extract information from the face and palmprint images in the form of sub block windows. Multiresolution analysis using Gabor transform and DCT is computed for each sub block window to produce compact local features for the face and palmprint images. Multiresolution Gabor analysis captures important information in the texture of the images while DCT represents the information in different frequency components. Important features with high discrimination power are then preserved by selecting several low frequency coefficients in order to estimate the model parameters. The local features extracted are fused in a new matrix interleaved method. The new fused feature vector is higher in dimensionality compared to the original feature vectors from both modalities, thus it carries high discriminating power and contains rich statistical information. The fused feature vector also has larger data points in the feature space which is advantageous for the training process using statistical methods. The underlying statistical information in the fused feature vectors is captured using GMM where several numbers of modal parameters are estimated from the distribution of fused feature vector. Maximum likelihood score is used to measure a degree of certainty to perform recognition while maximum likelihood score normalization is used for classification process. The use of likelihood score normalization is found to be able to suppress an imposter likelihood score when the background model parameters are estimated from a pool of users which include statistical information of an imposter. The present method achieved the highest recognition accuracy 97% and 99.7% when tested using FERET-PolyU dataset and ORL-PolyU dataset respectively.Universiti Malaysia Perlis and Ministry of Higher Education Malaysi

Sistema biométrico multimodal baseado em pupilometria dinâmica

Orientador : Prof. Dr. Alessandro ZimmerDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduaçao em Engenharia Elétrica. Defesa: Curitiba, 20/12/2011Bibliografia: fls. 113-121Resumo: A identificação pessoal através de características biométricas, ou seja, de medidas e atributos relacionados ao corpo humano, comparada ao uso de senhas, chaves ou documentos, apresenta a vantagem de não depender da posse de algo ou de um conhecimento que pode ser perdido ou roubado. No entanto, a maioria dos traços individuais usados nestes tipos de sistemas está sujeita a fraudes, através da reprodução das características em meios artificiais, como fotografias, próteses e gravações. A identificação baseada em características dinâmicas e reflexos humanos, tais como os movimentos sacádicos do olho e a contração pupilar, é uma alternativa que pode resolver este tipo de problema, mas a quantidade de informação envolvida não é suficiente para discriminar indivíduos. O uso conjunto de mais de uma característica biométrica, na chamada biometria multimodal, também é uma forma de dificultar a ocorrência de evasão. Neste trabalho de pesquisa, foi estudada a possibilidade de desenvolvimento de um sistema biométrico multimodal menos propenso a fraudes, baseado tanto nas características do reflexo pupilar à luz quanto na textura da íris. Um método de geração do vetor de primitivas e comparação de amostras foi proposto e testado por 59 voluntários, apresentando resultados promissores para esta nova abordagem. No melhor caso, a taxa de erros iguais ficou em 2,44%.Abstract: Personal identification using biometric features, i.e. measurements and attributes of the human body, compared to the use of passwords, key or documents, has the advantage of being independent of some object or some knowledge that can be lost or stolen. However, most of the individual features used in these types of systems are subject to frauds, by reproducing the human characteristics by artificial means, such as pictures, prosthesis and recordings. Identification based on dynamic characteristics and human reflexes, like saccadic movements and the pupil constriction, is an alternative that can solve this kind of problem, but the amount of information is usually not enough to identify a person. The use of multiple biometric characteristics, which is named multimodal biometrics, is also a way to avoid the occurrence of fraud, by involving more than one individual feature. In this research work, the possibility of developing a multimodal biometrics system less subject to fraud was studied based on the characteristics of the pupillary light reflex and the iris texture. A method for the generation of a vector of features and for the comparison of samples has been proposed and tested by 59 volunteers, leading to promising results for this new approach. In the best case, the equal error rate was 2.44%