A processor implemented method for signature verification, wherein the method comprises: monitoring finger movement associated with a signature gesture of a subject for a predefined time window, based on a shadow sensing mechanism utilizing a sensor array and ambient light; extracting a signature data for the subject from the sensor array for the predefined time window, at regular predefined time instants, wherein the extracted signature data for the subject is represented as a matrix with each row of the matrix representing signature data corresponding to each time instant and each column representing status of each sensor from the sensor array for corresponding time instant; pre-processing the extracted signature data by differentiating the matrix, row-wise and column-wise, to generate a row difference matrix and a column difference matrix; determining: an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix; and a plurality of signature parameters based on the column difference matrix and the row difference matrix, wherein the plurality of signature parameters comprise for each sensor, a temporal variability derived from the column difference matrix, a positional variability derived from the row difference matrix, and a distance value of a distance parameter that maximizes an optimization function, wherein the distance value of the distance parameter is derived by solving the optimization function, wherein the optimization function includes the distance parameter, a penalty term, a matching signature covariance matrix, a nonmatching signature covariance matrix, and a constant value; and analysing the idle signature time fraction and the plurality of signature parameters of the subject being monitored, based on a Common Spatial Pattern, CSP, based Support Vector Machine, SVM, classifier, wherein the CSP based SVM classifier performs online classification of the extracted signature data into one of a matching signature class and a non-matching signature class with respect to a subject of interest. monitoring finger movement associated with a signature gesture of a subject for a predefined time window, based on a shadow sensing mechanism utilizing a sensor array and ambient light; extracting a signature data for the subject from the sensor array for the predefined time window, at regular predefined time instants, wherein the extracted signature data for the subject is represented as a matrix with each row of the matrix representing signature data corresponding to each time instant and each column representing status of each sensor from the sensor array for corresponding time instant; pre-processing the extracted signature data by differentiating the matrix, row-wise and column-wise, to generate a row difference matrix and a column difference matrix; determining: an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix; and a plurality of signature parameters based on the column difference matrix and the row difference matrix, wherein the plurality of signature parameters comprise for each sensor, a temporal variability derived from the column difference matrix, a positional variability derived from the row difference matrix, and a distance value of a distance parameter that maximizes an optimization function, wherein the distance value of the distance parameter is derived by solving the optimization function, wherein the optimization function includes the distance parameter, a penalty term, a matching signature covariance matrix, a nonmatching signature covariance matrix, and a constant value; and an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix; and a plurality of signature parameters based on the column difference matrix and the row difference matrix, wherein the plurality of signature parameters comprise for each sensor, a temporal variability derived from the column difference matrix, a positional variability derived from the row difference matrix, and a distance value of a distance parameter that maximizes an optimization function, wherein the distance value of the distance parameter is derived by solving the optimization function, wherein the optimization function includes the distance parameter, a penalty term, a matching signature covariance matrix, a nonmatching signature covariance matrix, and a constant value; and analysing the idle signature time fraction and the plurality of signature parameters of the subject being monitored, based on a Common Spatial Pattern, CSP, based Support Vector Machine, SVM, classifier, wherein the CSP based SVM classifier performs online classification of the extracted signature data into one of a matching signature class and a non-matching signature class with respect to a subject of interest.