Covariate Analysis for View-point Independent Gait Recognition

Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KNN classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment

Productivity and firm selection: intra- vs international trade

Recent theoretical models predict gains from international trade coming from intra-industry reallocations, due to a firm selection effect. In this paper we answer two related questions. First, what is the magnitude of this selection effect, and how does it compare to that of intra-national trade? Second, would the removal of 'behind-the-border' trade frictions between integrated EU countries lead to large productivity gains? To answer these questions, we extend and calibrate the Melitz and Ottaviano (2007) model on productivity and trade data for European economies in 2000, and simulate counterfactual trade liberalization scenarios. We consider 11 EU countries and a total of 31 economies, including 21 French regions. Our first result is that, in the French case, international trade has a sizeable impact on aggregate productivity, but smaller than that of intra-national trade. Second, substantial productivity gains (around 20%) can be expected from 'behind-the-border' integration. In both experiments, we predict the corresponding variations in average prices, markups, quantities and profits. We show that the model fits sales and exports data reasonably well, and we perform a number of robustness checks. We also suggest some explanations for the substantial cross-economy and cross-industry variations in our estimates of productivity gains, highlighting the importance of accessibility and competitiveness.European integration, intra-national trade, firm-level data, firm selection, gains from trade, total factor productivity

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

Productivity and Firm Selection: Quantifying the “New” Gains from Trade

We discuss how standard computable equilibrium models of trade policy can be enriched with selection effects without missing other important channels of adjustment. This is achieved by estimating and simulating a partial equilibrium model that accounts for a number of real world effects of trade liberalisation: richer availability of product varieties; tougher competition and weaker market power of firms; better exploitation of economies of scale; and, of course, efficiency gains via the selection of the most efficient firms. The model is estimated on E.U. data and simulated in counterfactual scenarios that capture several dimensions of European integration. Simulations suggest that the gains from trade are much larger in the presence of selection effects. Even in a relatively integrated economy as the E.U., dismantling residual trade barriers would deliver relevant welfare gains stemming from lower production costs, smaller markups, lower prices, larger firm scale and richer product variety. We believe our analysis provides enough ground to support the inclusion of firm heterogeneity and selection effects in the standard toolkit of trade policy evaluation.European Integration, Firm-level Data, Firm Selection, Gains from Trade, Total Factor Productivity

Long-Run Patterns of Demand: The Expenditure System of the CDES Indirect Utility Function - Theory and Applications

In this paper, we unify and extend the analytical and empirical application of the ”indirect addilog” expenditure system, introduced by Leser (1941), Somermeyer- Wit (1956) and Houthakker (1960). Using the Box-Cox transform, we present a parametric analysis of the Houthakker specification of the fundamental indirect utility function - called the CDES specification (constant differences of Allen elasticities of substitution) by Hanoch (1975). It is shown that the CDES demand system is less restrictive than implied by standard parameter restrictions in the literature, Hanoch (1975), Deaton & Muellbauer (1980), or else neither adequately indicated, Houthakker (1960), Silberberg & Suen (2001). Our parametric examination implies that Marshallian own-price elasticities are no longer restricted to being all larger than one in absolute value; hence CDES can now naturally exhibit both the inelastic and elastic own price elasticities of observable (Marshallian) demands. Furthermore, we argue that in computable general equilibrium models (CGE), the CDES compares favorably with other expenditure systems, e.g. the linear expenditure system (LES), since CDES and LES need the same outside information for calibration of the parameters, but CDES is not confined to constancy of marginal budget shares (linear Engel curves). Moreover, we show that the non-homothetic CDES preferences are a simple and natural extension of the homothetic CES (constant elasticities of substitution) preferences, and, accordingly, CDES can more realistically be used in specifying CGE models with a demand side of non-unitary income elasticities. A succint theoretical briefing of the CDES history with general and concise formulas is offered. We illustrate CDES estimation and the calculation of a comprehensive set of income and price elasticities by applying CDES to Danish budget survey data. With a large number budget items included, coherent numerical values for the income, own, and cross price elasticities, as shown here, seem nowhere calculated and available in the voluminous literature.CDES demand systems, non-homothetic preferences, general price elasticities, CGE modeling, budget data implementation

Firm Productivity in Bangladesh Manufacturing Industries

The author studies the determinants of total factor productivity (TFP) for manufacturing firms in Bangladesh using data from a recent survey. She obtains TFP measures by making use of firm-specific deflators for output and inputs. Controlling for industry, location, and year fixed effects, she finds that: (1) firm size and TFP are negatively correlated; (2) firm age and TFP exhibit an inverse-U shaped relationship; (3) TFP improves with the quality of the firm's human capital; (4) global integration improves TFP; (5) firms with research and development activities and quality certifications have higher TFP, while more advanced technologies improve TFP only in the presence of significant absorptive capacity; (6) power supply problems cost firms heavily in terms of TFP losses; and (7) the presence of crime dampens TFP.Water and Industry,Economic Growth,Microfinance,Small Scale Enterprise,Economic Theory&Research

Extending quality and covariate analyses for gait biometrics

Recognising humans by the way they walk has attracted a significant interest in recent years due to its potential use in a number of applications such as automated visual surveillance. Technologies utilising gait biometrics have the potential to provide safer society and improve quality of life. However, automated gait recognition is a very challenging research problem and some fundamental issues remain unsolved.At the moment, gait recognition performs well only when samples acquired in similar conditions are matched. An operational automated gait recognition system does not yet exist. The primary aim of the research presented in this thesis is to understand the main challenges associated with deployment of gait recognition and to propose novel solutions to some of the most fundamental issues. There has been lack of understanding of the effect of some subject dependent covariates on gait recognition performance. We have proposed a novel dataset that allows analyses of various covariates in a principled manner. The results of the database evaluation revealed that elapsed time does not affect recognition in the short to medium term, contrary to what other studies have concluded. The analyses show how other factors related to the subject affect recognition performance.Only few gait recognition approaches have been validated in real world conditions. We have collected a new dataset at two realistic locations. Using the database we have shown that there are many environment related factors that can affect performance. The quality of silhouettes has been identified as one of the most important issues for translating gait recognition research to the ‘real-world’. The existing quality algorithms proved insufficient and therefore we extended quality metrics and proposed new ways of improving signature quality and therefore performance. A new fully working automated system has been implemented.Experiments using the system in ‘real-world’ conditions have revealed additional challenges not present when analysing datasets of fixed size. In conclusion, the research has investigated many of the factors that affect current gait recognition algorithms and has presented novel approaches of dealing with some of the most important issues related to translating gait recognition to real-world environments

Covariate conscious approach for Gait recognition based upon Zernike moment invariants

Gait recognition i.e. identification of an individual from his/her walking pattern is an emerging field. While existing gait recognition techniques perform satisfactorily in normal walking conditions, there performance tend to suffer drastically with variations in clothing and carrying conditions. In this work, we propose a novel covariate cognizant framework to deal with the presence of such covariates. We describe gait motion by forming a single 2D spatio-temporal template from video sequence, called Average Energy Silhouette image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the parts of AESI infected with covariates. Following this, features are extracted from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of Directional Pixels (MDPs) methods. The obtained features are fused together to form the final well-endowed feature set. Experimental evaluation of the proposed framework on three publicly available datasets i.e. CASIA dataset B, OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently published gait recognition approaches, prove its superior performance.Comment: 11 page

Errors in survey reports of consumption expenditures

This paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States. The Interview sample follows survey households over four calendar quarters and consists of retrospectively asked information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks and includes detailed information about frequently purchased items (food, personal cares and household supplies). Each survey has its own questionnaire and sample. Information from one sample is exploited as an instrument for the other sample to derive a correction for the measurement error affecting observed measures of consumption inequality. Implications of our ?ndings are used as a test for the permanent income hypothesis.Consumption Inequality; Measurement Error; Permanent Income Hypothesis