Cyclostationary Processes on Shape Spaces for Gait-Based Recognition

Abstract. We present a geometric and statistical approach to gaitbased human recognition. The novelty here is to consider observations of gait, considered as planar silhouettes, to be cyclostationary processes on a shape space of simple closed curves. Consequently, gait analysis reduces to quantifying differences between underlying stochastic processes using their observations. Individual shapes can be compared using geodesic lengths, but the comparison of gait cycles requires tools for extraction, interpolation, registration, and averaging of individual gait cycles before comparisons. The main steps in our approach are: (i) off-line extraction of human silhouettes from IR video data, (ii) use of piecewise-geodesic paths, connecting the observed shapes, to smoothly interpolate between them, (iii) computation of an average gait cycle within class (i.e. associated with a person) using Karcher means, (iv) registration of average cycles using linear and nonlinear time scaling, (iv) comparisons of average cycles using geodesic lengths between the corresponding shapes. We illustrate this approach on gait sequence obtained from infrared video clips. Experimental results are presented for a data set of 26 subjects.

Model-Based Approaches for Predicting Gait Changes over Time

Interest in automated biometrics continues to increase, but has little consideration of time which are especially important in surveillance and scan control. This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when $6$ or $12$ months have passed between recording of the gallery and the probe sets. Moreover, in some cases some extra covariates present as well. We have shown previously how recognition rates fall significantly when data is captured between lengthy time intervals. Under the assumption that it is possible to have some subjects from the probe for training and that similar subjects have similar changes in gait over time, we suggest predictive models of changes in gait due both to time and now to time-invariant covariates. Our extended time-dependent predictive model derives high recognition rates when time-dependent or subject-dependent covariates are added. However it is not able to cope with time-invariant covariates, therefore a new time-invariant predictive model is suggested to accommodate extra covariates. These are combined to achieve a predictive model which takes into consideration all types of covariates. A considerable improvement in recognition capability is demonstrated, showing that changes can be modelled successfully by the new approach

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Oil volatility, oil and gas firms and portfolio diversification

This paper investigates the volatility spillovers and co-movements among oil prices and stock prices of major oil and gas corporations over the period between 18th June 2001 and 1st February 2016. To do so, we use the spillover index approach by Diebold and Yilmaz (2009, 2012, 2014, 2015) and the dynamic correlation coefficient model of Engle (2002) so as to identify the transmission mechanisms of volatility shocks and the contagion of volatility among oil prices and stock prices of oil and gas companies, respectively. Given that volatility transmission across oil and major oil and gas corporations is important for portfolio diversification and risk management, we also examine optimal weights and hedge ratios among the aforementioned series. Our results point to the existence of significant volatility spillover effects among oil and oil and gas companies’ stock volatility. However, the spillover is usually unidirectional from oil and gas companies’ stock volatility to oil volatility, with BP, CHEVRON, EXXON, SHELL and TOTAL being the major net transmitters of volatility to oil markets. Conditional correlations are positive and time-varying, with those between each of the aforementioned companies and oil being the highest. Finally, the diversification benefits and hedging effectiveness based on our results are discussed

Extraction of bodily features for gait recognition and gait attractiveness evaluation

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1319-2. Copyright @ 2012 Springer.Although there has been much previous research on which bodily features are most important in gait analysis, the questions of which features should be extracted from gait, and why these features in particular should be extracted, have not been convincingly answered. The primary goal of the study reported here was to take an analytical approach to answering these questions, in the context of identifying the features that are most important for gait recognition and gait attractiveness evaluation. Using precise 3D gait motion data obtained from motion capture, we analyzed the relative motions from different body segments to a root marker (located on the lower back) of 30 males by the fixed root method, and compared them with the original motions without fixing root. Some particular features were obtained by principal component analysis (PCA). The left lower arm, lower legs and hips were identified as important features for gait recognition. For gait attractiveness evaluation, the lower legs were recognized as important features.Dorothy Hodgkin Postgraduate Award and HEFCE

Motives for corporate cash holdings:the CEO optimism effect

We examine the chief executive officer (CEO) optimism effect on managerial motives for cash holdings and find that optimistic and non-optimistic managers have significantly dissimilar purposes for holding more cash. This is consistent with both theory and evidence that optimistic managers are reluctant to use external funds. Optimistic managers hoard cash for growth opportunities, use relatively more cash for capital expenditure and acquisitions, and save more cash in adverse conditions. By contrast, they hold fewer inventories and receivables and their precautionary demand for cash holdings is less than that of non-optimistic managers. In addition, we consider debt conservatism in our model and find no evidence that optimistic managers’ cash hoarding is related to their preference to use debt conservatively. We also document that optimistic managers hold more cash in bad times than non-optimistic managers do. Our work highlights the crucial role that CEO characteristics play in shaping corporate cash holding policy