Basic technologies for fingerprint feature extraction and matching have been improved to such a stage that they can be embedded into commercial Automatic Fingerprint Verification Systems (AFVSs). However, the reliability of AFVSs has kept attracting concerns from the society since AFVSs do fail occasionally due to difficulties like problematic fingers, changing environments, and malicious attacks. Furthermore, the absence of a solid theoretical foundation for evaluating AFVSs prevents these failures from been predicted and evaluated. Under the traditional empirical AFVS evaluation framework, repeated verification experiments, which can be very time consuming, have to be performed to test whether an update to an AFVS can really lead to an upgrade in its performance. Also, empirical verification results are often unable to provide deeper understanding of AFVSs. To solve these problems, we propose a novel statistical evaluation model for minutiae-based AFVSs based on the understanding of fingerprint minutiae patterns. This model can predict the verification performance metrics as well as their confidence intervals. The analytical power of our evaluation model, which makes it superior to empirical evaluation methods, can assist system developers to upgrade their AFVSs purposefully. Also, our model can facilitate the theoretical analysis of the advantages and disadvantages of various fingerprint verification techniques. We verify our claims through different and extensive experiments. / Chen, Jiansheng. / "November 2006." / Adviser: Yiu-Sang Moon. / Source: Dissertation Abstracts International, Volume: 68-08, Section: B, page: 5343. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 110-122). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Personal identification of individuals is becoming increasingly adopted in society today. Due to the large number of electronic systems that require human identification, faster and more secure identification systems are pursued. Biometrics is based upon the physical characteristics of individuals; of these the fingerprint is the most common as used within law enforcement. Fingerprint-based systems have been introduced into the society but have not been well received due to relatively high rejection rates and false acceptance rates. This limited acceptance of fingerprint identification systems requires new techniques to be investigated to improve this identification method and the acceptance of the technology within society. Electronic fingerprint identification provides a method of identifying an individual within seconds quickly and easily. The fingerprint must be captured instantly to allow the system to identify the individual without any technical user interaction to simplify system operation. The performance of the entire system relies heavily on the quality of the original fingerprint image that is captured digitally. A single fingerprint scan for verification makes it easier for users accessing the system as it replaces the need to remember passwords or authorisation codes. The identification system comprises of several components to perform this function, which includes a fingerprint sensor, processor, feature extraction and verification algorithms. A compact texture feature extraction method will be implemented within an embedded microprocessor-based system for security, performance and cost effective production over currently available commercial fingerprint identification systems. To perform these functions various software packages are available for developing programs for windows-based operating systems but must not constrain to a graphical user interface alone. MATLAB was the software package chosen for this thesis due to its strong mathematical library, data analysis and image analysis libraries and capability. MATLAB enables the complete fingerprint identification system to be developed and implemented within a PC environment and also to be exported at a later date directly to an embedded processing environment. The nucleus of the fingerprint identification system is the feature extraction approach presented in this thesis that uses global texture information unlike traditional local information in minutiae-based identification methods. Commercial solid-state sensors such as the type selected for use in this thesis have a limited contact area with the fingertip and therefore only sample a limited portion of the fingerprint. This limits the number of minutiae that can be extracted from the fingerprint and as such limits the number of common singular points between two impressions of the same fingerprint. The application of texture feature extraction will be tested using variety of fingerprint images to determine the most appropriate format for use within the embedded system. This thesis has focused on designing a fingerprint-based identification system that is highly expandable using the MATLAB environment. The main components that are defined within this thesis are the hardware design, image capture, image processing and feature extraction methods. Selection of the final system components for this electronic fingerprint identification system was determined by using specific criteria to yield the highest performance from an embedded processing environment. These platforms are very cost effective and will allow fingerprint-based identification technology to be implemented in more commercial products that can benefit from the security and simplicity of a fingerprint identification system.

<p>This master thesis evaluates the use of off-the-shelf standard cameras for biometric identification of the human iris. As demands on secure identification are constantly rising and as the human iris provides with a pattern that is excellent for identification, the use of inexpensive equipment could help iris recognition become a new standard in security systems. To test the performance of such a system a review of the current state of the research in the area was done and the most promising methods were chosen for evaluation. A test environment based on open source code was constructed to measure the performance of iris recognition methods, image quality and recognition rate.</p><p>In this paper the image quality of a database consisting of images from a standard camera is assessed, the most important problem areas identified, and the overall recognition performance measured. Iris recognition methods found in literature are tested on this class of images. These together with newly developed methods show that a system using standard equipment can be constructed. Tests show that the performance of such a system is promising.</p>

An in-depth study of Advance Passenger Information and Passenger Name Record has never been accomplished prior to the events of September 11 th. It is of great importance to distinguish both of these concepts as they entail different legal consequence. API is to be understood as a data transmission that Border Control Authorities possess in advance in order to facilitate the movements of passengers. It is furthermore imperative that harmonization and inter-operability between States be achieved in order for this system to work. Although the obligations seem to appear for air carriers to be extraneous, the positive impact is greater than the downfalls. / Passenger Name Record access permits authorities to have additional data that could identify individuals requiring more questioning prior to border control clearance. This data does not cause in itself privacy issues other than perhaps the potential retention and manipulation of information that Border Control Authorities may acquire. In essence, bilateral agreements between governments should be sought in order to protect national legislation. / The common goal of the airline industry is to ensure safe and efficient air transport. API and PNR should be viewed as formalities that can facilitate border control clearance and prevent the entrance of potentially high-risk individuals.

Thesis (M.S.)--Rochester Institute of Technology, 2008. / Typescript. Includes bibliographical references (leaves 69-70).

Thesis (M.S.)--Florida State University, 2005. / Advisor: Dr. Mike Burmester, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed June 13, 2005). Document formatted into pages; contains viii, 35 pages. Includes bibliographical references.

Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2007. / The face is one of the most characteristic parts of the human body and has been used by people for personal identification for centuries. In this thesis an automatic process for frontal face recognition from 2–dimensional images is presented based on principal component analysis. The goal is to use these concepts in eventual face–recognizing login software. The first step is detecting faces in images that are allowed a certain degree of clutter. This is achieved by skin colour detection in the HSV colourspace. This process indicates the area of the image most likely corresponding to the face. Extracting the face is achieved by morphological processing of this area of the image. The face is then normalized by a transformation that uses the eye coordinates as input. Automatic eye detection is implemented based on colour analysis of the facial images and a 91.1% success rate is achieved. Recognition of the normalized faces is achieved using eigenfaces. To calculate these, a large enough database of facial images is needed. The xm2vts database is used in this thesis as the images have very constant lighting conditions throughout – an important factor affecting the accuracy of the recognition stage. Distinction is also made between identification and verification of faces. For identification, up to 80.1% accuracy is achieved, while for verification, the equal error rate is approximately 3.5%.

With the growing installation of surveillance video cameras in both private and public areas, it is an immediate requirement to develop intelligent video analysis system for the large-scale camera network. As a prerequisite step of person tracking and person retrieval in intelligent video analysis, person re-identification, which targets in matching person images across camera views is an important topic in computer vision community and has been received increasing attention in the recent years. In the supervised learning methods, the person re-identification task is formulated as a classification problem to extract matched person images/videos (positives) from unmatched person images/videos (negatives). Although the state-of-the-art supervised classification models could achieve encouraging re-identification performance, the assumption that label information is available for all the cameras, is impractical in large-scale camera network. That is because collecting the label information of every training subject from every camera in the large-scale network can be extremely time-consuming and expensive. While the unsupervised learning methods are flexible, their performance is typically weaker than the supervised ones. Though sufficient labels of the training subjects are not available from all the camera views, it is still reasonable to collect sufficient labels from a pair of camera views in the camera network or a few labeled data from each camera pair. Along this direction, we address two scenarios of person re-identification in large-scale camera network in this thesis, i.e. unsupervised domain adaptation and semi-supervised learning and proposed three methods to learn discriminative model using all available label information and domain knowledge in person re-identification. In the unsupervised domain adaptation scenario, we consider data with sufficient labels as the source domain, while data from the camera pair missing label information as the target domain. A novel domain adaptive approach is proposed to estimate the target label information and incorporate the labeled data from source domain with the estimated target label information for discriminative learning. Since the discriminative constraint of Support Vector Machines (SVM) can be relaxed into a necessary condition, which only relies on the mean of positive pairs (positive mean), a suboptimal classification model learning without target positive data can be those using target positive mean. A reliable positive mean estimation is given by using both the labeled data from the source domain and potential positive data selected from the unlabeled data in the target domain. An Adaptive Ranking Support Vector Machines (AdaRSVM) method is also proposed to improve the discriminability of the suboptimal mean based SVM model using source labeled data. Experimental results demonstrate the effectiveness of the proposed method. Different from the AdaRSVM method that using source labeled data, we can also improve the above mean based method by adapting it onto target unlabeled data. In more general situation, we improve a pre-learned classifier by adapting it onto target unlabeled data, where the pre-learned classifier can be domain adaptive or learned from only source labeled data. Since it is difficult to estimate positives from the imbalanced target unlabeled data, we propose to alternatively estimate positive neighbors which refer to data close to any true target positive. An optimization problem for positive neighbor estimation from unlabeled data is derived and solved by aligning the cross-person score distributions together with optimizing for multiple graphs based label propagation. To utilize the positive neighbors to learn discriminative classification model, a reliable multiple region metric learning method is proposed to learn a target adaptive metric using regularized affine hulls of positive neighbors as positive regions. Experimental results demonstrate the effectiveness of the proposed method. In the semi-supervised learning scenario, we propose a discriminative feature learning using all available information from the surveillance videos. To enrich the labeled data from target camera pair, image sequences (videos) of the tagged persons are collected from the surveillance videos by human tracking. To extract the discriminative and adaptable video feature representation, we propose to model the intra-view variations by a video variation dictionary and a video level adaptable feature by multiple sources domain adaptation and an adaptability-discriminability fusion. First, a novel video variation dictionary learning is proposed to model the large intra-view variations and solved as a constrained sparse dictionary learning problem. Second, a frame level adaptable feature is generated by multiple sources domain adaptation using the variation modeling. By mining the discriminative information of the frames from the reconstruction error of the variation dictionary, an adaptability-discriminability (AD) fusion is proposed to generate the video level adaptable feature. Experimental results demonstrate the effectiveness of the proposed method.

Biometric systems are being increasingly used, from daily entertainment to critical applications such as security access and identity management. It is known that biometric systems should meet the stringent requirement of low error rate. In addition, for critical applications, the security and privacy issues of biometric systems are required to be concerned. Otherwise, severe consequence such as the unauthorized access (security) or the exposure of identity-related information (privacy) can be caused. Therefore, it is imperative to study the vulnerability to potential attacks and identify the corresponding risks. Furthermore, the countermeasures should also be devised and patched on the systems. In this thesis, we study the security and privacy issues in biometric systems. We first make an attempt to reconstruct raw biometric data from biometric templates and demonstrate the security and privacy issues caused by the data reconstruction. Then, we make two attempts to protect biometric templates from being reconstructed and improve the state-of-the-art biometric template protection techniques.

Capes / Esta pesquisa estuda a utilização da rede neural Máquina de Aprendizado Extremo (ELM) para identificação de pessoas (biometria) através do eletrocardiograma (ECG). Os dados biométricos oferecem um nível elevado de segurança para a identificação de pessoas, e o ECG é uma técnica emergente e em crescente desenvolvimento. A ELM foi pouco empregada em sistemas de reconhecimento de padrões que utilizam o sinal de ECG. Desta forma, foram estudadas as técnicas de processamento de sinal: a Transformada Wavelet e a Análise dos Componentes Principais (PCA), com o objetivo de tratar e reduzir a dimensionalidade dos dados de entrada, bem como, fazer um estudo comparativo entre a ELM e a Percepetron Múltiplas Camadas (Multilayer Perceptron – MLP). Os testes foram realizados com 90 pessoas, o sinal de ECG utilizado é referente à derivação I contendo 500 amostras/s e 12-bits de resolução dentro de uma faixa nominal de ±10mV de variação, o número de registros variou de 2 a 20 para cada pessoa. O tamanho de cada ciclo completo de ECG para o processo de formação do espaço amostral foi definido de duas formas: 167 amostras contendo as ondas P+QRS e 280 amostras contendo as ondas P+QRS+T, dos quais foram utilizados os 10 ciclos que possuíam o mais elevado nível de similaridade. Com a Transformada Wavelet, o sinal de ECG foi decomposto em 3 níveis, onde para as ondas P+QRS as reduções foram de 86, 45 e 25 amostras, e para as ondas P+QRS+T foram de 142, 73 e 39 amostras. Já para o PCA o sinal foi reduzido de 10 ciclos cardíacos para apenas 1. Estes foram apresentadas a rede formando os conjuntos de treinamento e teste. Foram utilizadas as Redes Neurais Artificiais ELM e MLP para classificação do ECG. Os resultados obtidos comprovaram que a ELM pode ser utilizada para identificação de pessoas. / This research studies the use of neural network Extreme Learning Machine (ELM) to identify individuals (biometrics) by electrocardiogram (ECG). Biometric data offer a high level of security for identifying people, and ECG is an emerging technique and increasing development. ELM was little used in pattern recognition systems that use the ECG signal. In this way, the signal processing techniques were studied: Wavelet Transform and Principal Component Analysis (PCA), with the objective of treating and reducing the dimensionality of the input data, as was as, to make a comparative study between the ELM and Multilayer Perceptron (MLP). The tests were performed with 90 people, the ECG signal used is related to the lead I containing 500 samples/s and 12- bit resolution within a nominal range of ±10 mV of variation, the number of records ranged from 2 to 20 for each people. The size of each ECG cycle to complete the process of forming the sample space defined in two ways: 167 samples containing the P+QRS waves and 280 samples containing the P+QRS+T waves, of which 10 cycles were used to had the highest level of similarity. With the Wavelet Transform, the ECG signal was decomposed into 3 levels, where for the P+QRS waves the reductions were 86, 45 and 25 samples, and for the P+QRS+T waves were 142, 73 and 39 samples. For PCA, the signal for reduced from 10 cardiac cycles to only 1. These were presented to network forming the joint training and testing. The Artificial Neural Networks ELM and MLP were used for ECG classification. The results obtained proved that the ELM may be used to identify individuals.