Micro-expression Recognition using Spatiotemporal Texture Map and Motion Magnification

Micro-expressions are short-lived, rapid facial expressions that are exhibited by individuals when they are in high stakes situations. Studying these micro-expressions is important as these cannot be modified by an individual and hence offer us a peek into what the individual is actually feeling and thinking as opposed to what he/she is trying to portray. The spotting and recognition of micro-expressions has applications in the fields of criminal investigation, psychotherapy, education etc. However due to micro-expressions’ short-lived and rapid nature; spotting, recognizing and classifying them is a major challenge. In this paper, we design a hybrid approach for spotting and recognizing micro-expressions by utilizing motion magnification using Eulerian Video Magnification and Spatiotemporal Texture Map (STTM). The validation of this approach was done on the spontaneous micro-expression dataset, CASMEII in comparison with the baseline. This approach achieved an accuracy of 80% viz. an increase by 5% as compared to the existing baseline by utilizing 10-fold cross validation using Support Vector Machines (SVM) with a linear kernel

Computing a k-sparse n-length Discrete Fourier Transform using at most 4k samples and O(k log k) complexity

Given an $n$-length input signal \mbf{x}, it is well known that its Discrete Fourier Transform (DFT), \mbf{X}, can be computed in $O(n \log n)$ complexity using a Fast Fourier Transform (FFT). If the spectrum \mbf{X} is exactly $k$-sparse (where $k<<n$), can we do better? We show that asymptotically in $k$ and $n$, when $k$ is sub-linear in $n$ (precisely, $k \propto n^{\delta}$ where $0 < \delta <1$), and the support of the non-zero DFT coefficients is uniformly random, we can exploit this sparsity in two fundamental ways (i) {\bf {sample complexity}}: we need only $M=rk$ deterministically chosen samples of the input signal \mbf{x} (where $r < 4$ when $0 < \delta < 0.99$); and (ii) {\bf {computational complexity}}: we can reliably compute the DFT \mbf{X} using $O(k \log k)$ operations, where the constants in the big Oh are small and are related to the constants involved in computing a small number of DFTs of length approximately equal to the sparsity parameter $k$. Our algorithm succeeds with high probability, with the probability of failure vanishing to zero asymptotically in the number of samples acquired, $M$.Comment: 36 pages, 15 figures. To be presented at ISIT-2013, Istanbul Turke

Ontology-Based Context-Aware Service Discovery for Pervasive Environments

Existing service discovery protocols use a service matching process in order to offer services of interest to the clients. Potentially, the context information of the services and client can be used to improve the quality of service matching. To make use of context information in service matching, service discovery needs to address certain challenges. Firstly, it is required that the context information should have unambiguous representation. Secondly, the mobile devices should be able to disseminate context information seamlessly in the fixed network. And thirdly, dynamic nature of the context information should be taken into account. The proposed Context Aware Service Discovery (CASD) architecture deals with these challenges by means of an ontological representation and processing of context information, a concept of nomadic mobile context source and a mechanism of persistent service discovery respectively. This paper discusses proposed CASD architecture, its implementation and suggests further enhancements

Matamata Piako District: Socio-Demographic Profile 1986-2031

Matamata-Piako District has a larger proportion of those of European/New Zealand/Other ethnicity than either the Waikato Region or Total New Zealand, and a smaller proportion of both Maori and Pacific Island than the Waikato. Matamata-Piako also has substantially fewer people of Asian origin. In all cases, the number in each ethnic group has grown except for European/NZ/other. This group saw a small decline in its number in the 1996-2006 period, while Maori grew by 8 per cent, accounting for approximately 34 per cent of Matamata-Piako District’s growth, compared with 16 per cent of the Waikato’s

Matamata Piako District: Demographic Profile 1986 - 2031

This report outlines the demographic changes that have occurred in Matamata -Piako District, as well as what trends are expected in the future

A socio-demographic profile of Māori living in Australia

This report provides a comprehensive demographic and socio-economic profile of the Māori population in Australia using data from the 2011 Australia Census of Population and Housing. The purpose is to provide an evidence base with which to inform future policy approaches with respect to Māori in Australia. It focuses on five key areas: Population size and composition; Identity and culture; Year of arrival and citizenship; Education and work; Lone parenting and unpaid childcare. Comparisons are undertaken with Māori in the 2006 Australia Census, as well as with two reference groups: the total Australia population and migrant non-Māori New Zealanders. Where appropriate, we also distinguish Māori migrants born in New Zealand and Māori born in Australia. This captures important differences within the Māori population in Australia that have been under-examined in previous studies

Waitomo District: Demographic profile 1986-2031

This report outlines the demographic changes that have occurred in Waitomo Region, as well as what trends are expected in the future