Automatic Kinship Verification in Unconstrained Faces using Deep Learning

Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. Identifying kinship relations has also garnered interest due to several potential applications in security and surveillance and organizing and tagging the enormous number of videos being uploaded on the Internet. This dissertation has a five-fold contribution where first, a study is conducted to gain insight into the kinship verification process used by humans. Besides this, two separate deep learning based methods are proposed to solve kinship verification in images and videos. Other contributions of this research include interlinking face verification with kinship verification and creation of two kinship databases to facilitate research in this field. WVU Kinship Database is created which consists of multiple images per subject to facilitate kinship verification research. Next, kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250,000 still frames. In this dissertation, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determines their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender, age, and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d′, and perceptual information entropy. Next, utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as the output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. The results show that the proposed deep learning framework (KVRL-fcDBN) yields state-of-the-art kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Additionally, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields videobased kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases. Finally, we end by discussing the connections between face verification and kinship verification research. We explore the area of self-kinship which is age-invariant face recognition. Further, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification. By addressing several problems of limited samples per kinship dataset, introducing real-world variations in unconstrained databases and designing two deep learning frameworks, this dissertation improves the understanding of kinship verification across humans and the performance of automated systems. The algorithms proposed in this research have been shown to outperform existing algorithms across six different kinship databases and has till date the best reported results in this field

Anthropology and business: reflections on the business applications of cultural anthropology.

Today's business have international and intercultural dimensions. The complexity of market, organizational climate and culture and the management of human resources demand interdisciplinary and intercultural approach which are available in anthropological researches and methods. The consumer world has its own developments, diversifications and psycho-cultural fermentation. These changes pose new challenges for the designers and suppliers of products, services, systems and processes. Many changes in the economic and social spheres are beyond the range of conventional number-led, straight-line anaysis and planning. Rapid discontinuous changes defy all straight-line forecasting and conventional plannings. Qualitative and open-ended researches, scenario planning and brainstorming sessions, which skilled anthropologists are able to provide, are necessary to face such challenges.

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Inuit Approaches to Naming and Distinguishing Caribou: Considering Language, Place, and Homeland toward Improved Co-management

Qikiqtaq (King William Island), in the Kitikmeot region of Nunavut, has been largely overlooked in caribou research to date. Qikiqtaq is shown as blank, or as having uncertain status, in the majority of caribou herd range maps.Qikiqtaq (King William Island), in the Kitikmeot region of Nunavut, has been largely overlooked in caribou research to date. Qikiqtaq is shown as blank, or as having uncertain status, in the majority of caribou herd range maps. However, our work with Inuit Elders and hunters in Uqsuqtuuq (Gjoa Haven) on the southeastern coast of Qikiqtaq made it clear that caribou migrate on and off the island seasonally, and some remain on the island year-round. Caribou were identified as a local research priority in 2010, and we have worked together with Uqsuqtuurmiut (people of Uqsuqtuuq) from 2011 to 2016 to document and share Uqsuqtuurmiut knowledge of caribou movements, hunting, and habitat, as well as the importance of caribou for community diets, livelihoods, and cultural practices. In this process, it was important to understand appropriate Inuktitut terminology and local approaches to naming and distinguishing caribou in the region. Uqsuqtuurmiut do not generally distinguish caribou (tuktuit in Inuktitut) according to herds, in the way that biologists or wildlife managers do. Locally, people differentiate four main types of caribou: iluiliup tuktuit (inland caribou), kingailaup tuktuit (island caribou), qungniit (reindeer), and a mixture of iluiliup tuktuit and kingailaup tuktuit. Through these names, along with reviewing approaches to naming and distinguishing caribou in other Kitikmeot and Kivalliq communities, we emphasize how Inuit-caribou connections are articulated and enacted through language, place, and homeland. In efforts to support more inclusive and meaningful incorporation of Inuit knowledge in caribou co-management, we suggest that careful consideration of Inuit approaches to naming and distinguishing caribou could aid communication and mutual understanding. Key considerations that emerged include (1) accounting for dialectical differences, (2) understanding relative geographic references, and (3) recognizing historical and contemporary influences of traditional homelands and societies on terminology used. These considerations have potential implications for identifying and discussing caribou, as well as for new or refined approaches to monitoring caribou herds and habitats, since these approaches are often the result of how herds are defined.Jusqu’à présent, les recherches sur le caribou ont largement fait abstraction de l’île Qikiqtaq (île du Roi-Guillaume), dans la région de Kitikmeot, au Nunavut. La majorité des cartes montrant l’aire de répartition du caribou laissent l’île Qikiqtaq en blanc, ou indiquent que son statut est incertain. Toutefois, notre travail auprès d’aînés et de chasseurs inuits à Uqsuqtuuq (Gjoa Haven) sur la côte sud-est de Qikiqtaq a bien prouvé que les caribous migrent sur l’île et en repartent de façon saisonnière, et que certains restent sur l’île toute l’année. Le caribou a été décrété comme sujet de recherche prioritaire à l’échelle locale en 2010, et de 2011 à 2016, nous avons travaillé en collaboration avec les Uqsuqtuurmiut (le peuple d’Uqsuqtuuq) pour documenter et partager les connaissances des Uqsuqtuurmiut sur les déplacements, la chasse et l’habitat des caribous, ainsi que l’importance du caribou pour le régime alimentaire des gens, les moyens de subsistance et les pratiques culturelles. Dans le cadre de ce processus, il était important de comprendre la terminologie appropriée en inuktitut et les approches locales prises pour nommer et distinguer les espèces de caribous de la région. Les Uqsuqtuurmiut ne distinguent généralement pas le caribou (tuktuit en inuktitut) par hardes, comme le font les biologistes ou les gestionnaires de la faune. À l’échelle locale, les gens distinguent quatre principaux types de caribous : le caribou des terres intérieures (iluiliup tuktuit), le caribou des îles (kingailaup tuktuit), le renne (qungniit) et un mélange d’iluiliup tuktuit et de kingailaup tuktuit. En ayant recours à ces noms, ainsi qu’en revoyant les approches employées pour nommer et distinguer le caribou dans les autres collectivités de Kitikmeot et de Kivalliq, nous mettons l’accent sur la façon dont les relations entre les Inuit et les caribous se manifestent et sont exprimées selon la langue, l’endroit et la patrie. Dans le but d’appuyer l’intégration plus inclusive et significative des connaissances des Inuit aux fins de la cogestion du caribou, nous croyons qu’un examen attentif des approches utilisées par les Inuit pour nommer et distinguer les caribous pourrait faciliter la communication et la compréhension mutuelle. Les principales considérations qui en découlent sont : 1) la prise en compte des différences de dialecte, 2) la compréhension des références géographiques relatives et 3) la reconnaissance des influences historiques et contemporaines des patries et sociétés traditionnelles sur la terminologie employée. Ces considérations ont des répercussions potentielles sur l’identification du caribou et les discussions à leur sujet, ainsi que sur l’établissement d’approches nouvelles et plus perfectionnées pour surveiller les hardes et les habitats de caribous, puisque ces approches sont souvent le résultat de la façon dont les hardes sont définies

Voicing Kinship with Machines: Diffractive Empathetic Listening to Synthetic Voices in Performance.

This thesis contributes to the field of voice studies by analyzing the design and production of synthetic voices in performance. The work explores six case studies, consisting of different performative experiences of the last decade (2010- 2020) that featured synthetic voice design. It focusses on the political and social impact of synthetic voices, starting from yet challenging the concepts of voice in the machine and voice of the machine. The synthetic voices explored are often playing the role of simulated artificial intelligences, therefore this thesis expands its questions towards technology at large. The analysis of the case studies follows new materialist and posthumanist premises, yet it tries to confute the patriarchal and neoliberal approach towards technological development through feminist and de-colonial approaches, developing a taxonomy for synthetic voices in performance. Chapter 1 introduces terms and explains the taxonomy. Chapter 2 looks at familiar representations of fictional AI. Chapter 3 introduces headphone theatre exploring immersive practices. Chapters 4 and 5 engage with chatbots. Chapter 6 goes in depth exploring Human and Artificial Intelligence interaction, whereas chapter 7 moves slightly towards music production and live art. The body of the thesis includes the work of Pipeline Theatre, Rimini Protokoll, Annie Dorsen, Begüm Erciyas, and Holly Herndon. The analysis is informed by posthumanism, feminism, and performance studies, starting from my own practice as sound designer and singer, looking at aesthetics of reproduction, audience engagement, and voice composition. This thesis has been designed to inspire and provoke practitioners and scholars to explore synthetic voices further, question predominant biases of binarism and acknowledge their importance in redefining technology

A Continuous-Time Microsimulation and First Steps Towards a Multi-Level Approach in Demography

Microsimulation is a methodology that closely mimics life-course dynamics. In this thesis, we describe the development of the demographic microsimulation with a continuous time scale that we have realized in the context of the project MicMac - Bridging the micro-macro gap in population forecasting. Furthermore, we detail extensions that we have added to the initial version of the MicMac microsimulation.Mikrosimulation ist eine Prognosetechnik, die sich hervorragend eignet, um Bevölkerungsdynamik realitätsnah abzubilden. In dieser Dissertation beschreiben wir die Entwicklung einer demografischen Mikrosimulation, die wir im Rahmen des Projektes MicMac - Bridging the micro-macro gap in population forecasting erstellt haben. Zudem erläutern wir Erweiterungen, die wir an der ursprünglichen MicMac- Mikrosimulation vorgenommen haben