3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

The ghosts that visit us as we dream and Figurative homelands: second-generation immigrant experiences in North American contemporary poetry

The Ghosts That Visit Us as We Dream is a poetic manuscript that observes my family’s immigration from Tanzania to Canada. The poems are voiced from a variety of familial perspectives to capture how identity reforms and transforms throughout generations. Several of the poems were written as part of my research trip to Tanzania in January 2019, while others meditate on my experiences of growing up in Alberta, Canada. This manuscript principally employs repetitions and figurations of the natural world to reflect on wider themes of womanhood, belonging, cultural dissonance, loss, homeland, and spirituality. Water, in all its forms, becomes one of the collection’s major metaphors, representing liminality, crossings, and time as recursive. My work traverses the borders between the imaginary, the inherited, and the present moment. Figurative Homelands: Second-generation Immigrant Experiences in North American Contemporary Poetry examines how second-generation immigrants figuratively represent their North American and ancestral homelands in poetry. It includes a critical analysis of the poetic works of South Asian, Muslim second-generation immigrants, Kazim Ali, Fatimah Asghar and Tarfia Faizullah, and evaluates how they fuse the literal with the figurative in order to explore, give expression to, and take ownership of multidimensional identities. It examines the poetics of diaspora, specifically considering second-generation immigrant diasporic identities, from a multidirectional approach. Moreover, it builds upon the framework of what Sadia Abbas calls the “new Islam,” and examines how figurative homelands are constructed within the context of conflictual experiences arising from Islamophobia in the period following 9/11. This research also evaluates how second-generation immigrants craft figurative homelands using intergenerational storytelling and childhood remembrances. Additionally, it examines how loss manifests for writers who live in liminality, and how contradictory experiences or multiplicity are illustrated by gaps in both a poem’s formal structure and its conceptual landscape

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

Reciprocity and reputation: a review of direct and indirect social information gathering

Direct reciprocity, indirect reciprocity, and reputation are important interrelated topics in the evolution of sociality. This non-mathematical review is a summary of each. Direct reciprocity (the positive kind) has a straightforward structure (e.g., “A rewards B, then B rewards A”) but the allocation might differ from the process that enabled it (e.g., whether it is true reciprocity or some form of mutualism). Indirect reciprocity (the positive kind) occurs when person (B) is rewarded by a third party (A) after doing a good deed towards somebody else (C) — with the structure “A observes B help C, therefore A helps B.” Here too, the allocation differs from the process: if there is underlying cognition, then indirect reciprocity is based on some ability to keep track of the reputations of others (to remember that “B helped C”). Reputation is a kind of social impression based on typicality, derived from three channels of experience (direct encounters, bystander observation, and gossip). Although non-human animals cannot gossip verbally, they can eavesdrop on third parties and learn vicariously. This paper ends with a proposal to investigate the topic of social expertise as a model for understanding how animals understand and utilise observed information within their social groups

Reciprocity and reputation: a review of direct and indirect social information gathering

Direct reciprocity, indirect reciprocity, and reputation are important interrelated topics in the evolution of sociality. This non-mathematical review is a summary of each. Direct reciprocity (the positive kind) has a straightforward structure (e.g., “A rewards B, then B rewards A”) but the allocation might differ from the process that enabled it (e.g., whether it is true reciprocity or some form of mutualism). Indirect reciprocity (the positive kind) occurs when person (B) is rewarded by a third party (A) after doing a good deed towards somebody else (C) — with the structure “A observes B help C, therefore A helps B.” Here too, the allocation differs from the process: if there is underlying cognition, then indirect reciprocity is based on some ability to keep track of the reputations of others (to remember that “B helped C”). Reputation is a kind of social impression based on typicality, derived from three channels of experience (direct encounters, bystander observation, and gossip). Although non-human animals cannot gossip verbally, they can eavesdrop on third parties and learn vicariously. This paper ends with a proposal to investigate the topic of social expertise as a model for understanding how animals understand and utilise observed information within their social groups

After Collective Memory: Postnational Europe and Socially Engaged Art

This thesis focuses on works of public art that enjoy proven success in challenging the national bias of European heritage practice. By developing methods at the intersection between collective memory, critical historiography, and theory, I situate heritage debates in relation to forms of discrimination that emerged as symptoms of the financial crisis (2008-present). I then describe how public art interventions help to unsettle the grand narratives of cosmopolitan idealism that work to neutralize anti-racist strategies in the public sphere. The progression of my thesis eventually poses a challenge to the cosmopolitan reach of the Jewish diasporic tradition in particular. To that end, I explore the archival strategies of Holocaust memory practitioners, including their express aim of including diverse (i.e. non-Jewish) histories of violent exclusion into the historical record; the social and political conditions for the emergence of counter-monuments in West Germany during the 1970s, and the subsequent efforts that were made to turn this memorial aesthetic into a global standard for the memory culture industry; the haunting resurgence of cosmopolitan aspirations in Yael Bartana’s video installation, And Europe Will Be Stunned (2011); and a meditation on Bartana’s attempt at revisiting the racial dynamics of intergenerational violence in the aftermath of genocide

Arts of the Impossible: Violence, Trauma, and Erasure in the Global South

This dissertation examines how contemporary Anglophone, Hispanophone, and Francophone literature from Africa, Latin America, the Caribbean, and South Asia (1984-present) reconfigures historical archives to negotiate the ethics of representing state violence in repressive societies. I identify new literary forms politically conscious writers are devising to capture and contest human rights violations. Using an interdisciplinary decolonial feminist framework, I closely read works by Cristina Peri Rossi, Michael Ondaatje, M. NourbeSe Philip, Edwidge Danticat, Boubacar Boris Diop, and Roberto Bolaño— a diverse set of postcolonial and post-dictatorship writers never before compared in comparative literature. I call these writers’ endeavors to reframe traumatic history “arts of the impossible,” which defy the alleged unrepresentability of collective trauma to secure justice and forestall impunity. I compare representations of wide-ranging atrocities including forced disappearance, slavery, genocide, and femicide— crimes exemplifying what I term “ontological erasure.” At stake in ontological erasure are not simply lost perspectives from multiply marginalized victims, like women and queer people of color, but the very possibility of citizenship and the will to dissent state recognition enables. To resist the threats posed by the authorization of these crimes to political freedom, these writers, I argue, reinvent evidentiary forms historically suppressed by authoritarian states, including court transcripts, testimonies, forensic reports, and national archives. These authors’ innovations push the boundaries of what counts as “evidence” in acts of state violence that are uniquely determined by erasure; they also imagine new methods for remembering past atrocities without compromising recognition for stigmatized minorities in the future

Modelling the Deformation, Recrystallization and Microstructure-Related Properties in Metals

In the special issue related to Modelling the Deformation, Recrystallization and Microstructure-Related Properties in Metals, we presented a wide spectrum of articles dealing with modelling of microstructural aspects involved in deformation and recrystallization as well as simulation of microstructure-based and texture-based properties in various metals. The latest advances in the theoretical interpretation of mesoscopic transformations based on experimental observations were partially discussed in the current special issue. The studies dealing with the modelling of structure-property relationships are likewise analyzed in the present collection of manuscripts. The contributions in the current collection evidently demonstrate that the properties of metallic materials are microstructure dependent and therefore the thermomechanical processing (TMP) of the polycrystalline aggregates should be strictly controlled to guarantee the desired bunch of qualities. Given this, the assessment of microstructure evolution in metallic systems is of extraordinary importance. Since the trial-error approach is a time-consuming and quite expensive methodology, the materials research community tends to employ a wide spectrum of computational approaches to simulate each chain of TMP and tune the processing variables to ensure the necessary microstructural state which will provide desired performance in the final product. Although many hidden facets of various technological processes and related microstructural changes were revealed in the submitted works by employing advanced computational approaches, nevertheless, the contributions collected in this issue clearly show that further efforts are required in the field of modelling to understand the complexity of material’s world. The final goal of modelling efforts might be a development of a comprehensive model, which will be capable of describing many aspects of microstructure evolution during thermomechanical processing