<p>In today’s society, people often find themselves in situations where they need to be identified;for example when we buy alcohol, need to use an ATM or log on to our e-mail account. The most common methods today that are used for these kinds of matters are antiquated, and in</p><p>the meantime, criminals all over the world get more sophisticated. Companies invest billions every day in order to protect their interests. Maybe it’s time that we finally give biometrics the</p><p>attention that it deserves.</p><p>This essay aims to describe the biometric methods that are available today, find some of the most effective when it comes to physical access and make a comparison. Face recognition and fingerprint scanning will be described more thoroughly. Pros and cons will be analyzed and the theory will be linked to interviews with three Swedish organisations.</p> / <p>I dagens samhälle hamnar vi människor regelbundet i situationer där vi blir tvungna att identifiera oss. Det kan till exempelvis vara när vi köper alkohol, tar ut pengar eller loggar in på vårt e-mailkonto. De vanligaste metoderna, som idag används för dessa identifieringar, har funnits länge och är föråldrade och i takt med detta blir brottslingar världen allt mer sofistikerade. Dagligen investerar företag världen över miljarder för att skydda exempelvis</p><p>data med hjälp av koder och andra mjukvaruinstallationer. Kanske är det istället dags för att vi ger biometrin en ordentlig chans.</p><p>Den här uppsatsen syftar till att redogöra för de biometriska säkerhetsmetoder som finns tillgängliga, identifiera några av de effektivaste när det gäller fysisk access och jämföra dessa med varandra. Det är framförallt ansiktsigenkänning och fingeravtrycksavläsning som kommer att ges mycket plats i studien. För- och nackdelar kommer att tas upp och teorin kommer att kopplas till intervjuer gjorda med tre svenska företag.</p>

Thesis (Ph. D.)--University of Florida, 2001. / Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Research on video-based face recognition has started getting increased attention in the past few years. Algorithms developed for video have an advantage from the availability of plentitude of frames in videos to extract information from. Despite this fact, most research in this direction has limited the scope of the problem to the application of still image-based approaches to some selected frames on which 2D algorithms are expected to perform well. It can be realized that such an approach only uses the spatial information contained in video and does not incorporate the temporal structure.Only recently has the intelligence community begun to approach the problem in this direction. Video-based face recognition algorithms in the last couple of years attempt to simultaneously use the spatial and temporal information for the recognition of moving faces. A new face recognition method that falls into the category of algorithms that adopt spatio-temporal representation and utilizes dynamic information extracted from video is presented. The method was designed based on the hypothesis that the strain pattern exhibited during facial expression provides a unique "fingerprint" for recognition. First, a dense motion field is obtained with an optical flow algorithm. A strain pattern is then derived from the motion field. In experiments with 30 subjects, results indicate that strain pattern is an useful biometric, especially when dealing with extreme conditions such as shadow light and face camouflage, for which conventional face recognition methods are expected to fail. The ability to characterize the face using the elastic properties of facial skin opens up newer avenues to the face recognition community in the context of modeling a face using features beyond visible cues.

Facial expression detection by a machine is a valuable topic for Human Computer Interaction and has been a study issue in the behavioural science for some time. Recently, significant progress has been achieved in machine analysis of facial expressions but there are still some interestes to study the area in order to extend its applications. This work investigates the theoretical concepts behind facial expressions and leads to the proposal of new algorithms in face detection and facial feature localisation, design and construction of a prototype system to test these algorithms. The overall goals and motivation of this work is to introduce vision based techniques able to detect and recognise the facial expressions. In this context, a facial expression prototype system is developed that accomplishes facial segmentation (i.e. face detection, facial features localisation), facial features extraction and features classification. To detect a face, a new simplified algorithm is developed to detect and locate its presence from the fackground by exploiting skin colour properties which are then used to distinguish between face and non-face regions. This allows facial parts to be extracted from a face using elliptical and box regions whose geometrical relationships are then utilised to determine the positions of the eyes and mouth through morphological operations. The mean and standard deviations of segmented facial parts are then computed and used as features for the face. For images belonging to the same class, thses features are applied to the K-mean algorithm to compute the controid point of each class expression. This is repeated for images in the same expression class. The Euclidean distance is computed between each feature point and its cluster centre in the same expression class. This determines how close a facial expression is to a particular class and can be used as observation vectors for a Hidden Markov Model (HMM) classifier. Thus, an HMM is built to evaluate an expression of a subject as belonging to one of the six expression classes, which are Joy, Anger, Surprise, Sadness, Fear and Disgust by an HMM using distance features. To evaluate the proposed classifier, experiments are conducted on new subjects using 100 video clips that contained a mixture of expressions. The average successful detection rate of 95.6% is measured from a total of 9142 frames contained in the video clips. The proposed prototype system processes facial features parts and presents improved results of facial expressions detection rather than using whole facial features as proposed by previous authors. This work has resulted in four contributions: the Ellipse Box Face Detection Algorithm (EBFDA), Facial Features Distance Algorithm (FFDA), Facial features extraction process, and Facial features classification. These were tested and verified using the prototype system.

Despite decades of effort in academia and industry, it is not yet possible to build machines that can replicate many seemingly-basic human perceptual abilities. This work focuses on the problem of face identification that most of us effortlessly solve daily. Substantial progress has been made towards the goal of automatically identifying faces under tightly controlled conditions; however, in the domain of unconstrained face images, many challenges remain. We observe that the recent combination of widespread digital photography, inexpensive digital storage and bandwidth, and online social networks has led to the sudden creation of repositories of billions of shared photographs and opened up an important new domain for unconstrained face identification research. Drawing upon the newly-popular phenomenon of “tagging,” we construct some of the first face identification datasets that are intended to model the digital social spheres of online social network members, and we examine various qualitative and quantitative properties of these image sets. The identification datasets we present here include up to 100 individuals, making them comparable to the average size of members’ networks of “friends” on a popular online social network, and each individual is represented by up to 100 face samples that feature significant real-world variation in appearance, expression, and pose. We demonstrate that biologically-inspired visual representations can achieve state-of-the-art face identification performance on our novel frontal and multi-pose face datasets. We also show that the addition of a tree-structured classifier and training set augmentation can enhance accuracy in the multi-pose setting. Finally, we illustrate that the machine-readable “social context” in which shared photos are often embedded can be applied to further boost face identification accuracy. Taken together, our results suggest that accurate automated face identification in vast online shared photo collections is now feasible. / Engineering and Applied Sciences

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

In this thesis, person identification by combining automatic face and iris recognition is analyzed. Person identification by his face is one of the most intuitive from all biometric measures. We are used to recognizing familiar faces and confirming identity by a short glance at one's id card which contains image of the face. We are also used to being observed by surveillance cameras, which can perform biometric authentication without even being noticed. However, facial biometrics is one of most unstable metrics because the face gets noticeably older in several years and can frequently change depending on the mood of its owner. The core algorithm for facial recognition presented in this work is based on Gabor features. Deep analysis of each step helped to develop the method with better or similar accuracy to the best published results received on the same datasets, while being simple and fast. On the other hand, person identification by his iris is one of the most sophisticated, stable and accurate biometrics. The core algorithm for iris recognition presented in this work is based on a novel iris texture representation by local extremum points of multiscale Taylor expansion. The proposed irises comparison method is very different from the classic phase-based methods, but is also fast and accurate. Combining it with our implementation of phase-based method results in superior recognition accuracy which is comparable or better than any published results received on the same... [to full text] / Darbe tyrinėjama asmens identifikacija, kombinuojant automatinį veido ir akies rainelės atpažinimą. Automatinė identifikacija pagal veidą yra intuityviausia iš biometrijos metrikų, kadangi būtent pagal veidą mes geriausiai sugebame atpažinti pažįstamus asmenis. Tai yra ir viena labiausiai priimtinų, kadangi visi esame įprate, kad mus filmuoja apsaugos kameros, lengviausiai išmatuojama - nes nereikalauja jokių įmantrių skanerių, tačiau kartu - tai yra ir viena iš nestabiliausių metrikų, kadangi veidas sensta ir šiaip kinta priklausomai nuo savininko nuotaikos. Darbe pristatomas veidų atpažinimo algoritmas paremtas Gaboro požymiais. Nuodugni analizė padėjo sukurti algoritmą, kurio tikslumą vertinant standartiniais testais jis lenkia arba yra lygus su geriausiais publikuotais metodais, tačiau pasižymi paprastumu ir dideliu greičiu. Tuo tarpu automatinė identifikacija pagal rainelę yra laikoma viena stabiliausių ir tiksliausių. Darbe pristatomas rainelių atpažinimo algoritmas naudoja naujovišką rainelių tekstūros vaizdavimo būdą, paremtą lokaliais dvimačiais funkcijų aproksimacijos Teiloro eilutėmis ekstremumais. Kartu pristatomas naudojamų požymių palyginimo metodas, kuris yra labai nutolęs nuo bet kokių iki šiol rainelių tekstūrų palyginimui naudojamų metodų. Pasiūlytas rainelių atpažinimo metodas vėlgi yra spartus ir itin tikslus, o sujungus su klasikinio stiliaus rainelių tekstūrų palyginimu tikslumu nenusileidžia geriausiems publikuotiems metodams. Darbas užbaigiamas veidų... [toliau žr. visą tekstą]

Darbe tyrinėjama asmens identifikacija, kombinuojant automatinį veido ir akies rainelės atpažinimą. Automatinė identifikacija pagal veidą yra intuityviausia iš biometrijos metrikų, kadangi būtent pagal veidą mes geriausiai sugebame atpažinti pažįstamus asmenis. Tai yra ir viena labiausiai priimtinų, kadangi visi esame įprate, kad mus filmuoja apsaugos kameros, lengviausiai išmatuojama - nes nereikalauja jokių įmantrių skanerių, tačiau kartu - tai yra ir viena iš nestabiliausių metrikų, kadangi veidas sensta ir šiaip kinta priklausomai nuo savininko nuotaikos. Darbe pristatomas veidų atpažinimo algoritmas paremtas Gaboro požymiais. Nuodugni analizė padėjo sukurti algoritmą, kurio tikslumą vertinant standartiniais testais jis lenkia arba yra lygus su geriausiais publikuotais metodais, tačiau pasižymi paprastumu ir dideliu greičiu. Tuo tarpu automatinė identifikacija pagal rainelę yra laikoma viena stabiliausių ir tiksliausių. Darbe pristatomas rainelių atpažinimo algoritmas naudoja naujovišką rainelių tekstūros vaizdavimo būdą, paremtą lokaliais dvimačiais funkcijų aproksimacijos Teiloro eilutėmis ekstremumais. Kartu pristatomas naudojamų požymių palyginimo metodas, kuris yra labai nutolęs nuo bet kokių iki šiol rainelių tekstūrų palyginimui naudojamų metodų. Pasiūlytas rainelių atpažinimo metodas vėlgi yra spartus ir itin tikslus, o sujungus su klasikinio stiliaus rainelių tekstūrų palyginimu tikslumu nenusileidžia geriausiems publikuotiems metodams. Darbas užbaigiamas veidų... [toliau žr. visą tekstą] / In this thesis, person identification by combining automatic face and iris recognition is analyzed. Person identification by his face is one of the most intuitive from all biometric measures. We are used to recognizing familiar faces and confirming identity by a short glance at one's id card which contains image of the face. We are also used to being observed by surveillance cameras, which can perform biometric authentication without even being noticed. However, facial biometrics is one of most unstable metrics because the face gets noticeably older in several years and can frequently change depending on the mood of its owner. The core algorithm for facial recognition presented in this work is based on Gabor features. Deep analysis of each step helped to develop the method with better or similar accuracy to the best published results received on the same datasets, while being simple and fast. On the other hand, person identification by his iris is one of the most sophisticated, stable and accurate biometrics. The core algorithm for iris recognition presented in this work is based on a novel iris texture representation by local extremum points of multiscale Taylor expansion. The proposed irises comparison method is very different from the classic phase-based methods, but is also fast and accurate. Combining it with our implementation of phase-based method results in superior recognition accuracy which is comparable or better than any published results received on the same... [to full text]

The field of eyewitness memory has long been concerned with identifications but functioned in the absence of an explanatory theory. Recently Charman and Wells (2007) developed applied lineup theory to address this deficiency. They argue that quality of memory and the decision process interact to determine lineup decision accuracy. In a series of experiments I tested whether their theoretical assumptions hold for face recognition tasks and tested the theory using simple manipulations with lineups. Experiments 1 through 7 utilized a face recognition paradigm. In Experiments 1 through 5, the relationship between quality of memory and face recognition accuracy was explored as a function of frequency of viewing, duration of viewing, and depth of processing. The results indicated that, as expected, increased frequency of viewing and deeper processing of faces at encoding led to better recognition memory. Unexpectedly, increasing the duration of viewing did not increase recognition memory. In the remaining experiments (Experiments 6 to 9) I manipulated the decision process by manipulating the match between a face image shown at encoding and retrieval and how quickly participants were able to respond. The results of Experiments 6 and 7 only weakly supported applied lineup theory. In Experiments 8 and 9 I used a lineup paradigm and again found little support for applied lineup theory. Notably, the manipulations of decision process were relatively unsuccessful in Experiments 6 to 9. The stimulus manipulations used may not have been sufficient to produce differences in the decision process or applied lineup theory may not account for lineup decisions. Suggestions for future research on lineup decision processes to clarify whether applied lineup theory can account for lineup decisions are made. / Thesis (Ph.D, Psychology) -- Queen's University, 2010-09-18 16:10:43.637