Naushad Mamode Khan

Representation of Hand Dorsal Vein Features Using a Low Dimensional Representation Integrating Cholesky Decomposition

Dorsal hand vein pattern is a promising biometric which is attracting the attention of researchers, of late, to provide more secure identification system. Different approaches have been developed to extract the vein pattern. However, there is a need to find more efficient methods which can reduce matching time. In this work, Principle Component Analysis (PCA), which is a successful method applied on human faces and hand geometry, is being modified based on Cholesky decomposition to represent low dimensional features of the vein pattern. Cholesky decomposition is used to simplify the matrices and it is noticed that there is no loss of information regarding the matching of the eigenveins. The time taken for the processing phase is reduced by 6s which is desirable when developing biometric security system. The system was tested successfully on a database of 200 images with a threshold value of 0.9.

Modelling a non-stationary BINAR(1) Poisson process

ABSTRACT Non-stationarity in bivariate time series of counts may be induced by a number of time-varying covariates affecting the bivariate responses due to which the innovation terms of the individual series as well as the bivariate dependence structure becomes non-stationary. So far, in the existing models, the innovation terms of individual INAR(1) series and the dependence structure are assumed to be constant even though the individual time series are non-stationary. Under this assumption, the reliability of the regression and correlation estimates is questionable. Besides, the existing estimation methodologies such as the conditional maximum likelihood (CMLE) and the composite likelihood estimation are computationally intensive. To address these issues, this paper proposes a BINAR(1) model where the innovation series follow a bivariate Poisson distribution under some non-stationary distributional assumptions. The method of generalized quasi-likelihood (GQL) is used to estimate the regression effects while the serial and bivariate correlations are estimated using a robust moment estimation technique. The application of model and estimation method is made in the simulated data. The GQL method is also compared with the CMLE, generalized method of moments (GMM) and generalized estimating equation (GEE) approaches where through simulation studies, it is shown that GQL yields more efficient estimates than GMM and equally or slightly more efficient estimates than CMLE and GEE.

Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series

ABSTRACT This article proposes a novel non-stationary BINMA time series model by extending two INMA processes where their innovation series follow the bivariate Poisson under time-varying moment assumptions. This article also demonstrates, through simulation studies, the use and superiority of the generalized quasi-likelihood (GQL) approach to estimate the regression effects, which is computationally less complicated as compared to conditional maximum likelihood estimation (CMLE) and the feasible generalized least squares (FGLS). The serial and bivariate dependence correlations are estimated by a robust method of moments.

A GQL estimation approach for analysing non-stationary over-dispersed BINAR(1) time series

ABSTRACT This paper proposes a generalized quasi-likelihood (GQL) function for estimating the vector of regression and over-dispersion effects for the respective series in the bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with Negative Binomial (NB) marginals. The auto-covariance function in the proposed GQL is computed using some ‘robust’ working structures. As for the BINAR(1) process, the inter-relation between the series is induced mainly by the correlated NB innovations that are subject to different levels of over-dispersion. The performance of the GQL approach is tested via some Monte-Carlo simulations under different combination of over-dispersion together with low and high serial- and cross-correlation parameters. The model is also applied to analyse a real-life series of day and night accidents in Mauritius.

Investigating linear discriminant analysis (LDA) on dorsal hand vein images

Hand vein biometrics is gaining popularity over other biometrics due to its uniqueness and stability. However, the variations of images at image capture process pose a challenge in the performance of a biometric security system. Different processing techniques applied so far on dorsal hand vein images cannot represent the different orientation of the dorsal hand vein patterns at image capture. In this view, linear discriminant analysis (LDA) is adopted to represent oriented vein images. This method handles the within-class scatter and the between class-scatter between image sets compared to other methods like principal component analysis (PCA) and Independent component analysis (ICA). It maximizes the ratio of between-class scatter to the within-class scatter and guarantees the maximal separability between the data. In this work, images are captured at varied angles between 0° and 45°. Both PCA and LDA have been implemented to determine their behavior on varied angled images. After experimentations with the methods, it can be concluded that LDA outperforms PCA on images captured at varied angled.

A non-stationary BINAR(1) process with negative binomial innovations for modeling the number of goals in the first and second half: The case of Arsenal Football Club

ABSTRACT Arsenal Football Club has been among the top four in the Premier League for long, but recently the clubs performance has been quite inconsistent. This article performs a regression analysis to determine the factors that could explain these inconsistencies using a simple non-stationary first-order bivariate integer-valued autoregressive process with negative binomial cross-correlated innovations (BINAR(1)NB). The estimation of parameters is performed using a generalized quasi-likelihood approach. A small simulation study is presented. The BINAR(1)NB is also compared with other bivariate time series models.

Analysing Factors Affecting Hand Biometrics during Image Capture

Abstract As more people are connected digitally, a highly automatic personal identification system is crucial. Dorsal hand vein biometric is an emerging biometric characteristic which is explored at its full swing. Although, researchers have deployed many hand biometrics using interesting techniques, it has not yet been accepted in many applications. Images capture is an important phase where the images obtained determine the performance of the biometric security system. Environmental factors and behavior of the subjects have an effect on image capture. In these work, different variables, that is, distance between camera and hand, the angle of deviation and the environmental temperature are controlled to capture images. The results are analysed and the effect of the variables have been depicted. It is deduced that image capture phase in biometric applications deserve more attention.

Use of vector divisions in solving quasi-likelihood equations for a Poisson model

Maximum-likelihood estimation technique is known to provide consistent and most efficient regression estimates but often this technique is tedious to implement, particularly in the modelling of correlated count responses. To overcome this limitation, researchers have developed semi- or quasi-likelihood functions that depend only on the correct specification of the mean and variance of the responses rather than on the distribution function. Moreover, quasi-likelihood estimation provides consistent and equally efficient estimates as the maximum-likelihood approach. Basically, the quasi-likelihood estimating function is a non-linear equation constituting of the gradient, Hessian and basic score matrices. Henceforth, to obtain estimates of the regression parameters, the quasi-likelihood equation is solved iteratively using the Newton–Raphson technique. However, the inverse of the Jacobian matrix involved in the Newton–Raphson method may not be easy to compute since the matrix is very close to singularity. In this paper, we consider the use of vector divisions in solving quasi-likelihood equations. The vector divisions are implemented to form secant method formulas. To assess the performance of the use of vector divisions with the secant method, we generate cross-sectional Poisson counts using different sets of mean parameters. We compute the estimates of the regression parameters using the Newton–Raphson technique and vector divisions and compare the number of non-convergent simulations under both algorithms.

A Flexible Observation-Driven Stationary Bivariate Negative Binomial INAR(1) with Non-homogeneous Levels of Over-dispersion

Abstract The existing bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with negative binomial (NB) innovations is developed under stationary moment conditions and in particular under same level of over-dispersion index. In this paper, we propose a flexible BINAR(1) under NB innovations where the counting series are subject to two different levels of over-dispersion under same stationary moment condition. The unknown parameters of the new model are estimated using a generalized quasi-likelihood (QL) estimating equation. The performance of this estimation method is assessed through some numerical experiments under different time dimensions.

A case study of MCB and SBMH stock transaction using a novel BINMA(1) with non-stationary NB correlated innovations

ABSTRACT This paper focuses on the modeling of the intra-day transactions at the Stock Exchange Mauritius (SEM) of the two major banking companies: Mauritius Commercial Bank Group Limited (MCB) and State Bank of Mauritius Holdings Ltd (SBMH) in Mauritius using a flexible non-stationary bivariate integer-valued moving average of order 1 (BINMA(1)) process with negative binomial (NB) innovations that may cater for different levels of over-dispersion. The generalized quasi-likelihood (GQL) approach is used to estimate the regression, dependence and over-dispersion effects. However, for the over-dispersion parameters, the auto-covariance structure in the GQL is constructed using some higher order moments. This new model is tested over some Monte-Carlo experiments and is applied to analyze the inter-related intra-day series of volume of stocks for the two banking institutions using data collected from 3 August to 16 October 2015 in the presence of some time-varying covariates such as the news effect, Friday effect and time of the day effect.