“…Features that capture significant global and local discriminatory information and the classification techniques that perform consistently with these extracted features have been used in the literature [2]. A wide range of classification techniques such as, artificial neural networks (ANN) [3], [4], [5], [6], [7], [8], meta classifiers [9], [10], [11], [12], [13], K-nearest neighbors [14], [15], [16], [17], [18] and support vector machines (SVM) [19], [20], [21], [22], [23], [24], [25], [26], [27] have been used for the PFR. Among the classifiers employed to tackle the PFR, using support vector machine have attained the best results [26], [27], [28], [29], [30], [31], [32].…”