A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons. Nowadays, there is a great variety of approaches and new techniques within the evolutionary computing area, and these approaches and techniques have emerged to help find optimal solutions to problems or models and bioinspired algorithms are part of this area. In this work a grey wolf optimizer is proposed for the design of modular granular neural networks, and the results are compared against a genetic algorithm and a firefly algorithm in order to know which of these techniques provides better results when applied to human recognition

A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira

Anomalous behaviour detection using heterogeneous data

Anomaly detection is one of the most important methods to process and find abnormal data, as this method can distinguish between normal and abnormal behaviour. Anomaly detection has been applied in many areas such as the medical sector, fraud detection in finance, fault detection in machines, intrusion detection in networks, surveillance systems for security, as well as forensic investigations. Abnormal behaviour can give information or answer questions when an investigator is performing an investigation. Anomaly detection is one way to simplify big data by focusing on data that have been grouped or clustered by the anomaly detection method. Forensic data usually consists of heterogeneous data which have several data forms or types such as qualitative or quantitative, structured or unstructured, and primary or secondary. For example, when a crime takes place, the evidence can be in the form of various types of data. The combination of all the data types can produce rich information insights. Nowadays, data has become ‘big’ because it is generated every second of every day and processing has become time-consuming and tedious. Therefore, in this study, a new method to detect abnormal behaviour is proposed using heterogeneous data and combining the data using data fusion technique. Vast challenge data and image data are applied to demonstrate the heterogeneous data. The first contribution in this study is applying the heterogeneous data to detect an anomaly. The recently introduced anomaly detection technique which is known as Empirical Data Analytics (EDA) is applied to detect the abnormal behaviour based on the data sets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev Inequality) can be applied to any data distribution. Then, the second contribution is applying image data. The image data is processed using pre-trained deep learning network, and classification is done using a support vector machine (SVM). After that, the last contribution is combining anomaly result from heterogeneous data and image recognition using new data fusion technique. There are five types of data with three different modalities and different dimensionalities. The data cannot be simply combined and integrated. Therefore, the new data fusion technique first analyses the abnormality in each data type separately and determines the degree of suspicious between 0 and 1 and sums up all the degrees of suspicion data afterwards. This method is not intended to be a fully automatic system that resolves investigations, which would likely be unacceptable in any case. The aim is rather to simplify the role of the humans so that they can focus on a small number of cases to be looked in more detail. The proposed approach does simplify the processing of such huge amounts of data. Later, this method can assist human experts in their investigations and making final decisions

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

A novel face recognition system in unconstrained environments using a convolutional neural network

The performance of most face recognition systems (FRS) in unconstrained environments is widely noted to be sub-optimal. One reason for this poor performance may be due to the lack of highly effective image pre-processing approaches, which are typically required before the feature extraction and classification stages. Furthermore, it is noted that only minimal face recognition issues are typically considered in most FRS, thus limiting the wide applicability of most FRS in real-life scenarios. Thus, it is envisaged that developing more effective pre-processing techniques, in addition to selecting the correct features for classification, will significantly improve the performance of FRS. The thesis investigates different research works on FRS, its techniques and challenges in unconstrained environments. The thesis proposes a novel image enhancement technique as a pre-processing approach for FRS. The proposed enhancement technique improves on the overall FRS model resulting into an increased recognition performance. Also, a selection of novel hybrid features has been presented that is extracted from the enhanced facial images within the dataset to improve recognition performance. The thesis proposes a novel evaluation function as a component within the image enhancement technique to improve face recognition in unconstrained environments. Also, a defined scale mechanism was designed within the evaluation function to evaluate the enhanced images such that extreme values depict too dark or too bright images. The proposed algorithm enables the system to automatically select the most appropriate enhanced face image without human intervention. Evaluation of the proposed algorithm was done using standard parameters, where it is demonstrated to outperform existing image enhancement techniques both quantitatively and qualitatively. The thesis confirms the effectiveness of the proposed image enhancement technique towards face recognition in unconstrained environments using the convolutional neural network. Furthermore, the thesis presents a selection of hybrid features from the enhanced image that results in effective image classification. Different face datasets were selected where each face image was enhanced using the proposed and existing image enhancement technique prior to the selection of features and classification task. Experiments on the different face datasets showed increased and better performance using the proposed approach. The thesis shows that putting an effective image enhancement technique as a preprocessing approach can improve the performance of FRS as compared to using unenhanced face images. Also, the right features to be extracted from the enhanced face dataset as been shown to be an important factor for the improvement of FRS. The thesis made use of standard face datasets to confirm the effectiveness of the proposed method. On the LFW face dataset, an improved performance recognition rate was obtained when considering all the facial conditions within the face dataset.Thesis (PhD)--University of Pretoria, 2018.CSIR-DST Inter programme bursaryElectrical, Electronic and Computer EngineeringPhDUnrestricte

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

On Security and Privacy for Networked Information Society : Observations and Solutions for Security Engineering and Trust Building in Advanced Societal Processes

Our society has developed into a networked information society, in which all aspects of human life are interconnected via the Internet — the backbone through which a significant part of communications traffic is routed. This makes the Internet arguably the most important piece of critical infrastructure in the world. Securing Internet communications for everyone using it is extremely important, as the continuing growth of the networked information society relies upon fast, reliable and secure communications. A prominent threat to the security and privacy of Internet users is mass surveillance of Internet communications. The methods and tools used to implement mass surveillance capabilities on the Internet pose a danger to the security of all communications, not just the intended targets. When we continue to further build the networked information upon the unreliable foundation of the Internet we encounter increasingly complex problems,which are the main focus of this dissertation. As the reliance on communication technology grows in a society, so does the importance of information security. At this stage, information security issues become separated from the purely technological domain and begin to affect everyone in society. The approach taken in this thesis is therefore both technical and socio-technical. The research presented in this PhD thesis builds security in to the networked information society and provides parameters for further development of a safe and secure networked information society. This is achieved by proposing improvements on a multitude of layers. In the technical domain we present an efficient design flow for secure embedded devices that use cryptographic primitives in a resource-constrained environment, examine and analyze threats to biometric passport and electronic voting systems, observe techniques used to conduct mass Internet surveillance, and analyze the security of Finnish web user passwords. In the socio-technical domain we examine surveillance and how it affects the citizens of a networked information society, study methods for delivering efficient security education, examine what is essential security knowledge for citizens, advocate mastery over surveillance data by the targeted citizens in the networked information society, and examine the concept of forced trust that permeates all topics examined in this work.Yhteiskunta, jossa elämme, on muovautunut teknologian kehityksen myötä todelliseksi tietoyhteiskunnaksi. Monet verkottuneen tietoyhteiskunnan osa-alueet ovat kokeneet muutoksen tämän kehityksen seurauksena. Tämän muutoksen keskiössä on Internet: maailmanlaajuinen tietoverkko, joka mahdollistaa verkottuneiden laitteiden keskenäisen viestinnän ennennäkemättömässä mittakaavassa. Internet on muovautunut ehkä keskeisimmäksi osaksi globaalia viestintäinfrastruktuuria, ja siksi myös globaalin viestinnän turvaaminen korostuu tulevaisuudessa yhä enemmän. Verkottuneen tietoyhteiskunnan kasvu ja kehitys edellyttävät vakaan, turvallisen ja nopean viestintäjärjestelmän olemassaoloa. Laajamittainen tietoverkkojen joukkovalvonta muodostaa merkittävän uhan tämän järjestelmän vakaudelle ja turvallisuudelle. Verkkovalvonnan toteuttamiseen käytetyt menetelmät ja työkalut eivät vain anna mahdollisuutta tarkastella valvonnan kohteena olevaa viestiliikennettä, vaan myös vaarantavat kaiken Internet-liikenteen ja siitä riippuvaisen toiminnan turvallisuuden. Kun verkottunutta tietoyhteiskuntaa rakennetaan tämän kaltaisia valuvikoja ja haavoittuvuuksia sisältävän järjestelmän varaan, keskeinen uhkatekijä on, että yhteiskunnan ydintoiminnot ovat alttiina ulkopuoliselle vaikuttamiselle. Näiden uhkatekijöiden ja niiden taustalla vaikuttavien mekanismien tarkastelu on tämän väitöskirjatyön keskiössä. Koska työssä on teknisen sisällön lisäksi vahva yhteiskunnallinen elementti, tarkastellaan tiukan teknisen tarkastelun sijaan aihepiirä laajemmin myös yhteiskunnallisesta näkökulmasta. Tässä väitöskirjassa pyritään rakentamaan kokonaiskuvaa verkottuneen tietoyhteiskunnan turvallisuuteen, toimintaan ja vakauteen vaikuttavista tekijöistä, sekä tuomaan esiin uusia ratkaisuja ja avauksia eri näkökulmista. Työn tavoitteena on osaltaan mahdollistaa entistä turvallisemman verkottuneen tietoyhteiskunnan rakentaminen tulevaisuudessa. Teknisestä näkökulmasta työssä esitetään suunnitteluvuo kryptografisia primitiivejä tehokkaasti hyödyntäville rajallisen laskentatehon sulautetuviiille järjestelmille, analysoidaan biometrisiin passeihin, kansainväliseen passijärjestelmään, sekä sähköiseen äänestykseen kohdistuvia uhkia, tarkastellaan joukkovalvontaan käytettyjen tekniikoiden toimintaperiaatteita ja niiden aiheuttamia uhkia, sekä tutkitaan suomalaisten Internet-käyttäjien salasanatottumuksia verkkosovelluksissa. Teknis-yhteiskunnallisesta näkökulmasta työssä tarkastellaan valvonnan teoriaa ja perehdytään siihen, miten valvonta vaikuttaa verkottuneen tietoyhteiskunnan kansalaisiin. Lisäksi kehitetään menetelmiä parempaan tietoturvaopetukseen kaikilla koulutusasteilla, määritellään keskeiset tietoturvatietouden käsitteet, tarkastellaan mahdollisuutta soveltaa tiedon herruuden periaatetta verkottuneen tietoyhteiskunnan kansalaisistaan keräämän tiedon hallintaan ja käyttöön, sekä tutkitaan luottamuksen merkitystä yhteiskunnan ydintoimintojen turvallisuudelle ja toiminnalle, keskittyen erityisesti pakotetun luottamuksen vaikutuksiin