This thesis studies rapid object detection, focusing on feature-based methods. Firstly, modifications of training and detection of the Viola-Jones method are made to improve performance and overcome some of the current limitations such as rotation, occlusion and articulation. New classifiers produced by training and by converting existing classifiers are tested in face detection and hand detection. Secondly, the nature of invariant features in terms of the computational complexity, discrimination power and invariance to rotation and scaling are discussed. A new feature extraction method called Concentric Discs Moment Invariants (CDMI) is developed based on moment invariants and summed-area tables. The dimensionality of this set of features can be increased by using additional concentric discs, rather than using higher order moments. The CDMI set has useful properties, such as speed, rotation invariance, scaling invariance, and rapid contrast stretching can be easily implemented. The results of experiments with face detection shows a clear improvement in accuracy and performance of the CDMI method compared to the standard moment invariants method. Both the CDMI and its variant, using central moments from concentric squares, are used to assess the strength of the method applied to hand-written digits recognition. Finally, the parallelisation of the detection algorithm is discussed. A new model for the specific case of the Viola-Jones method is proposed and tested experimentally. This model takes advantage of the structure of classifiers and of the multi-resolution approach associated with the detection method. The model shows that high speedups can be achieved by broadcasting frames and carrying out the computation of one or more cascades in each node.

This thesis studies rapid object detection, focusing on feature-based methods. Firstly, modifications of training and detection of the Viola-Jones method are made to improve performance and overcome some of the current limitations such as rotation, occlusion and articulation. New classifiers produced by training and by converting existing classifiers are tested in face detection and hand detection. Secondly, the nature of invariant features in terms of the computational complexity, discrimination power and invariance to rotation and scaling are discussed. A new feature extraction method called Concentric Discs Moment Invariants (CDMI) is developed based on moment invariants and summed-area tables. The dimensionality of this set of features can be increased by using additional concentric discs, rather than using higher order moments. The CDMI set has useful properties, such as speed, rotation invariance, scaling invariance, and rapid contrast stretching can be easily implemented. The results of experiments with face detection shows a clear improvement in accuracy and performance of the CDMI method compared to the standard moment invariants method. Both the CDMI and its variant, using central moments from concentric squares, are used to assess the strength of the method applied to hand-written digits recognition. Finally, the parallelisation of the detection algorithm is discussed. A new model for the specific case of the Viola-Jones method is proposed and tested experimentally. This model takes advantage of the structure of classifiers and of the multi-resolution approach associated with the detection method. The model shows that high speedups can be achieved by broadcasting frames and carrying out the computation of one or more cascades in each node.

Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 208-226.

Orientadores: Alexandre Xavier Falcão, Anderson de Rezende Rocha / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-23T15:41:33Z (GMT). No. of bitstreams: 1 Chiachia_Giovani_D.pdf: 4376963 bytes, checksum: 8f7d18d591f2a5d943313d89416f96d4 (MD5) Previous issue date: 2013 / Resumo: Os seres humanos são especialistas natos em reconhecimento de faces, com habilidades que excedem em muito as dos métodos automatizados vigentes, especialmente em cenários não controlados, onde não há a necessidade de colaboração por parte do indivíduo sendo reconhecido. No entanto, uma característica marcante do reconhecimento de face humano é que nós somos substancialmente melhores no reconhecimento de faces familiares, provavelmente porque somos capazes de consolidar uma grande quantidade de experiência prévia com a aparência de certo indivíduo e de fazer uso efetivo dessa experiência para nos ajudar no reconhecimento futuro. De fato, pesquisadores em psicologia têm até mesmo sugeridos que a representação interna que fazemos das faces pode ser parcialmente adaptada ou otimizada para rostos familiares. Enquanto isso, a situação análoga no reconhecimento facial automatizado | onde um grande número de exemplos de treinamento de um indivíduo está disponível | tem sido muito pouco explorada, apesar da crescente relevância dessa abordagem na era das mídias sociais. Inspirados nessas observações, nesta tese propomos uma abordagem em que a representação da face de cada pessoa é explicitamente adaptada e realçada com o intuito de reconhecê-la melhor. Apresentamos uma coleção de métodos de aprendizado que endereça e progressivamente justifica tal abordagem. Ao aprender e operar com representações específicas para face de cada pessoa, nós somos capazes de consistentemente melhorar o poder de reconhecimento dos nossos algoritmos. Em particular, nós obtemos resultados no estado da arte na base de dados PubFig83, uma desafiadora coleção de imagens instituída e tornada pública com o objetivo de promover o estudo do reconhecimento de faces familiares. Nós sugerimos que o aprendizado de representações específicas para face de cada pessoa introduz uma forma intermediária de regularização ao problema de aprendizado, permitindo que os classificadores generalizem melhor através do uso de menos |, porém mais relevantes | características faciais / Abstract: Humans are natural face recognition experts, far outperforming current automated face recognition algorithms, especially in naturalistic, \in-the-wild" settings. However, a striking feature of human face recognition is that we are dramatically better at recognizing highly familiar faces, presumably because we can leverage large amounts of past experience with the appearance of an individual to aid future recognition. Researchers in psychology have even suggested that face representations might be partially tailored or optimized for familiar faces. Meanwhile, the analogous situation in automated face recognition, where a large number of training examples of an individual are available, has been largely underexplored, in spite of the increasing relevance of this setting in the age of social media. Inspired by these observations, we propose to explicitly learn enhanced face representations on a per-individual basis, and we present a collection of methods enabling this approach and progressively justifying our claim. By learning and operating within person-specific representations of faces, we are able to consistently improve performance on both the constrained and the unconstrained face recognition scenarios. In particular, we achieve state-of-the-art performance on the challenging PubFig83 familiar face recognition benchmark. We suggest that such person-specific representations introduce an intermediate form of regularization to the problem, allowing the classifiers to generalize better through the use of fewer | but more relevant | face features / Doutorado / Ciência da Computação / Doutor em Ciência da Computação

M.Sc. (Computer Science) / A video conference is an interactive meeting between two or more locations, facilitated by simultaneous two-way video and audio transmissions. People in a video conference, also known as participants, join these video conferences for business and recreational purposes. In a typical video conference, we should properly identify and authenticate every participant in the video conference, if information discussed during the video conference is confidential. This prevents unauthorized and unwanted people from being part of the conference and exposing any confidential information during the video conference. Present existing video conferencing systems however, have problems in this area, resulting in some risks. These risks relate precisely to the lack of facilities to properly identify and authenticate participants, making it possible for unwanted/unauthorised participants to join the conference or masquerade as another participant. It is especially a problem, when facilitators or organisers are the only participants that know the authorised participants, or participants allowed in a video conference. In this dissertation, we review the risks that are present in video conferencing, and create a security system, (called BioVid) that mitigates the identification and authentication risks in video conferences. BioVid uses a Speeded-Up Robust Features or SURF-based face recognition approach, to identify and authenticate any participant in a video conference. BioVid continuously monitors the participants to check if masquerading has occurred and when it does detect an unauthorised participant, it informs the Service Provider. The Service Provider can then deal with the problem by either kicking the participant or asking the other participants to vote the unauthorised participant out of the video conference.

Algorithmic management systems organize many different kinds of work across domains, and have increasingly come under academic scrutiny. Under labels including gig work, piecemeal work, and platform labor, these systems have been richly theorized under disciplines including human-computer interaction, sociology, communications, economics, and labor law. When it comes to the relationships between such systems and their workers, current theory frames these interactions on a continuum between organizational control and worker autonomy. This has laid the groundwork for other ways of examining micro-level practices of workers under algorithmic management. As an alternative to the binary of control and autonomy, this dissertation takes its cue from feminist scholars in Science, Technology, and Society (STS) studies. Drawing on frameworks from articulation, repair, and mutual shaping, I examine workers’ interpretations and interactions, to ask how new subjectivities around identity and community emerge from these entanglements. To shed empirical light on these processes, this dissertation employs a mixed-methods research design examining the introduction of facial recognition into the sociotechnical systems of algorithmic management. Data include 22 in-person interviews with workers in New York City and Toronto, a survey of 100 workers in the United States who have been subjected to facial recognition, and analysis of over 2800 comments gathered from an online workers’ forum posted over the course of four years.Facial recognition, like algorithmic management, suffers from a lack of empirical, on-the-ground insights into how workers communicate, negotiate, and strategize around and through them. Interviews with workers reveals that facial recognition evokes polysemia, i.e. a number of distinct, yet interrelated interpretations. I find that for some workers, facial recognition means safety and security. To others it means violation of privacy and accusations of fraud. Some are impressed by the “science-fiction”-like capabilities of the system: “it’s like living in the future.” Others are wary, and science fiction becomes a vehicle to encapsulate their fears: “I’m in the [movie] The Minority Report.” For some the technology is hyper-powerful: “It feels like I’m always being watched,” yet others decry, “it’s an obvious façade.” Following interviews, I build a body of research using empirical methods combined with frameworks drawn from STS and organizational theory to illuminate workers’ perceptions and strategies negotiating their algorithmic managers. I operationalize Julian Orr’s studies of storytelling among Xerox technicians to analyze workers’ information-sharing practices in online forums, to better understand how gig workers, devices, forums, and algorithmic management systems engage in mutual shaping processes. Analysis reveals that opposing interpretations of facial recognition, rather than dissolving into consensus of “shared understanding,” continue to persist. Rather than pursuing and relying on shared understanding of their work to maintain relationships, workers under algorithmic management, communicating in online forums about facial recognition, elide consensus. After forum analysis, I then conduct a survey, to assess workers’ fairness perceptions of facial recognition targeting and verification. The goal of this research is to establish an empirical foundation to determine whether algorithmic fairness perceptions are subject to theories of bounded rationality and decision-making. Finally, for the last two articles, I turn back to the forums, to analyze workers’ experiences negotiating two other processes with threats or ramifications for safety, privacy, and risk. In one article, I focus on their negotiation of threats from scam attackers, and the use the forum itself as a “shared repertoire” of knowledge. In the other I use the forums as evidence to illuminate workers’ experiences and meaning-making around algorithmic risk management under COVID-19. In the conclusion, I engage in theory-building to examine how algorithmic management and its attendant processes demand that information-sharing mechanisms serve novel ends buttressing legitimacy and authenticity, in what I call “para-organizational” work, a world of work where membership and legitimacy are liminal and uncertain. Ultimately, this body of research illuminates mutual shaping processes in which workers’ practices, identity, and community are entangled with technological artifacts and organizational structures. Algorithmic systems of work and participants’ interpretations of, and interactions with, related structures and devices, may be creating a world where sharing information is a process wielded not as a mechanism of learning, but as one of belonging.

The aim of this thesis is to interpret Jan Zahradnicek's spacious poem The Sign of Power. The interpretation crystallizes around the motifs of dehumanisation (connected with Nietzsche's motif of nihilism and of the last man) of a man, the loss of a word, discontinuity, the loss of time, the human face, nothingness (specific Nothingness) and the possibility of salvation, connected with an awakening of the sight. There are two semantic lines essential for enlightening these motifs: Dante's Divine Comedy and Picard's works of the late 40s. Zahradnicek wrote The Sign of Power during 1950-1951, at the time of his intense work on the translation of Dante's Divine Comedy. The purpose of the first part of this thesis is to illustrate how strongly the Divine Comedy influenced the key motifs of The Sign of Power. The purpose of the second part of the thesis is to uncover a new semantic context for the interpretation of Zahradnicek's poem; the works of Swiss essayist, philosopher and poet Max Picard, which were of great importance for Zahradnicek's poem. I see the exposition of Picard's specific grasp of the key modern phenomena, which penetrated to Zahradnicek's poem, as the further objective of the work. The thesis is guided by the fundamental question of The Sign of Power - "what happened with a man" -,...

Across three chapters of this doctoral dissertation, I explore how machine learning algorithms can be used to efficiently acquire personal information and responsibly use it in decision making, in marketing and beyond. In the first chapter, I show that machine learning on consumer facial images can reveal a variety of personal information. I provide evidence that such information can be profitably used by marketers. I also investigate the mechanism behind how facial images reveal personal information. In the second chapter, I propose a new self-supervised deep reinforcement learning approach to question prioritization and questionnaire shortening and show it is competitive against benchmark methods. I use the proposed method to show that typical consumer data sets can be reconstructed well based on relatively small select subsets of their columns. The reconstruction quality grows logarithmically in the relative size of the column subset, implying diminishing returns on measurement. Thus, many long questionnaires could be shortened with minimal information loss, increasing the consumer research efficiency and enabling previously impossible multi-scale omnibus studies. In the third chapter, I present a method to speed up ranking under constraints for live ethical content recommendations by predicting, rather than finding exactly, the solution to the underlying time-intensive optimization problem. The approach enables solving larger-than-previously-reported constrained content-ranking problems in real time, within 50 milliseconds, as required to avoid the perception of latency by the users. The approach could also help speed up general assignment and matching tasks.

Актуальность магистерской диссертации заключается в использовании нейронных сетей для решения плохо формализованных задач в интеллектуальном анализе данных. Рост объемов информации, а также расширение круга технически сложных задач принятия решений требуют систематизации существующих методов и разработки новых методик и алгоритмов решения. В магистерской диссертации рассматривается возможность применения нейронной сети при решении задачи распознавания эмоций человека. Основной целью работы является выбор информационной модели нейросети и реализация алгоритма распознавания двигательной активности лица. Нейронная сеть должна быть оптимальна по внутренней структуре, способу управления информационными потоками между нейронами. Выбранная информационная модель будет использована для решения практической задачи. Основными задачами диссертационной работы являются: 1) Изучение существующих видов искусственных интеллектуальных систем, а также методов их функционирования. 2) Изучение основных видов информационных моделей искусственных нейронных сетей. Выбор оптимальной информационной модели нейронной сети для решения задачи распознавания эмоций. 3) Изучение существующих методов распознавания мимики и выделение универсальных методов среди них. 4) Реализация и описание алгоритма распознавания двигательной активности лица и решение практической задачи. Объектом данного исследования являются подходы, методы распознавания мимических выражений. Предметом исследования являются информационные модели искусственных нейронных сетей, а также описание алгоритма распознавания двигательной активности лица для решения практической задачи Научная новизна магистерской диссертации заключается в использовании технологий нейросетей (информационных моделей), а также системы двигательной активности лица для реализации алгоритма распознавания эмоций человека. Практическая значимость диссертационной работы: результаты работы могут быть использованы при решении задач интеллектуального анализа данных, в решении сложных технических задач видеоанализа. Результаты работы предполагают последующую реализацию собственной методики распознавания двигательной активности лица. / In the master's thesis, the possibility of using a neural network for solving the problem of recognizing human emotions is considered. The growth of information and expansion of the range of technically complex decision-making problems require the systematization of the existing methods and develop new methods and algorithms. The main goal of the work is the choice of the information model of the neural network and the implementation of the algorithm for recognizing the motor activity of the human face. The practical significance of the work is that the results of the work can be used in solving problems of data mining, in solving complex technical problems of video analysis. The results suggest the subsequent implementation of the recognition techniques of the motor activity of a human face.