Joint optimization of manifold learning and sparse representations for face and gesture analysis

Face and gesture understanding algorithms are powerful enablers in intelligent vision systems for surveillance, security, entertainment, and smart spaces. In the future, complex networks of sensors and cameras may disperse directions to lost tourists, perform directory lookups in the office lobby, or contact the proper authorities in case of an emergency. To be effective, these systems will need to embrace human subtleties while interacting with people in their natural conditions. Computer vision and machine learning techniques have recently become adept at solving face and gesture tasks using posed datasets in controlled conditions. However, spontaneous human behavior under unconstrained conditions, or in the wild, is more complex and is subject to considerable variability from one person to the next. Uncontrolled conditions such as lighting, resolution, noise, occlusions, pose, and temporal variations complicate the matter further. This thesis advances the field of face and gesture analysis by introducing a new machine learning framework based upon dimensionality reduction and sparse representations that is shown to be robust in posed as well as natural conditions. Dimensionality reduction methods take complex objects, such as facial images, and attempt to learn lower dimensional representations embedded in the higher dimensional data. These alternate feature spaces are computationally more efficient and often more discriminative. The performance of various dimensionality reduction methods on geometric and appearance based facial attributes are studied leading to robust facial pose and expression recognition models. The parsimonious nature of sparse representations (SR) has successfully been exploited for the development of highly accurate classifiers for various applications. Despite the successes of SR techniques, large dictionaries and high dimensional data can make these classifiers computationally demanding. Further, sparse classifiers are subject to the adverse effects of a phenomenon known as coefficient contamination, where for example variations in pose may affect identity and expression recognition. This thesis analyzes the interaction between dimensionality reduction and sparse representations to present a unified sparse representation classification framework that addresses both issues of computational complexity and coefficient contamination. Semi-supervised dimensionality reduction is shown to mitigate the coefficient contamination problems associated with SR classifiers. The combination of semi-supervised dimensionality reduction with SR systems forms the cornerstone for a new face and gesture framework called Manifold based Sparse Representations (MSR). MSR is shown to deliver state-of-the-art facial understanding capabilities. To demonstrate the applicability of MSR to new domains, MSR is expanded to include temporal dynamics. The joint optimization of dimensionality reduction and SRs for classification purposes is a relatively new field. The combination of both concepts into a single objective function produce a relation that is neither convex, nor directly solvable. This thesis studies this problem to introduce a new jointly optimized framework. This framework, termed LGE-KSVD, utilizes variants of Linear extension of Graph Embedding (LGE) along with modified K-SVD dictionary learning to jointly learn the dimensionality reduction matrix, sparse representation dictionary, sparse coefficients, and sparsity-based classifier. By injecting LGE concepts directly into the K-SVD learning procedure, this research removes the support constraints K-SVD imparts on dictionary element discovery. Results are shown for facial recognition, facial expression recognition, human activity analysis, and with the addition of a concept called active difference signatures, delivers robust gesture recognition from Kinect or similar depth cameras

Revealing and analyzing the shared structure of deep face embeddings

2022 Summer.Includes bibliographical references.Deep convolutional neural networks trained for face recognition are found to output face embeddings which share a fundamental structure. More specifically, one face verification model's embeddings (i.e. last--layer activations) can be compared directly to another model's embeddings after only a rotation or linear transformation, with little performance penalty. If only rotation is required to convert the bulk of embeddings between models, there is a strong sense in which those models are learning the same thing. In the most recent experiments, the structural similarity (and dissimilarity) of face embeddings is analyzed as a means of understanding face recognition bias. Bias has been identified in many face recognition models, often analyzed using distance measures between pairs of faces. By representing groups of faces as groups, and comparing them as groups, this shared embedding structure can be further understood. Specifically, demographic-specific subspaces are represented as points on a Grassmann manifold. Across 10 models, the geodesic distances between those points are expressive of demographic differences. By comparing how different groups of people are represented in the structure of embedding space, and how those structures vary with model designs, a new perspective on both representational similarity and face recognition bias is offered

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind

Proposed: Technical Communicators Collaborating with Educators to Develop a Better EFL Curriculum for Ecuadorian Universities

This policy and action research in the form of a case study of language policy in Ecuador posits, with a pragmatic view, that students’ backgrounds, prior knowledge, and learning objectives should significantly impact curriculum development. Applying principles of information development, such as conducting usability studies and generating appropriate user profiles, technical communicators produce user-friendly documentation. Pairing technical communicators with educators to collaborate in the parallel processes of information development and curriculum development may yield instructional materials more useful to students than currently available materials are. An etic perspective is appropriate for this study for it does not presuppose what the students’ learning objectives are. Two hundred seventy-nine students taking classes in English as a foreign language (EFL) at three Ecuadorian higher education institutions voluntarily responded to a convenience sample survey designed to learn what benefits the students hoped to obtain from their university-level study of the English language. If this knowledge of student needs was used, in part, to form user profiles prior to course design, it may likely result in a different iteration of EFL instruction than the one currently being shaped by publishers and the national government as well as previous iterations shaped by higher education institutions and instructors

Examining the Post September 11, 2001 Practices of Accredited and Non-accredited Law Enforcement Agencies in the Aspects of Training, Legal and Service Delivery

Homeland security measures and the preparation of local law enforcement are reviewed, in light of September 11, 2001, to determine their effect on the Constitutional rights of citizens, the delivery of law enforcement services, e.g., call response and the training methodologies, and the fiscal impact to fulfill the new policing mandates. The study reviews aggressive measures, normally vested with federal law enforcement agencies, to determine if local police are also utilizing similar methods for the sake of national security and if such measures are undermining ethical and legal practices previously exercised by local police. This research also examines the distinction between accredited and nonaccredited law enforcement agencies as it relates to post September llth practices. The findings were gained through interviews at 7 local police agencies of 8 law enforcement executives, who provided rich narrative perspectives showing that their agencies: (a) still abided by Constitutional principles by not engaging in bias-based policing; (b) the impact on calls for services was limited to a few weeks following 9/11, 6 months later were back to normal, and are currently at pre-9/11 levels; (c) training practices have changed; and (d) little or no funding was received by the agencies from the federal government for training or equipment and the fiscal impact was either absorbed by the agency or equipment was not obtained