European Journal of Scientific Research - EuroJournals

Feature Selection Based on Statistical Analysis 427
Keywords: Statistical Analysis, ANOVA, Principal Component Analysis, Artificial
Neural Network.
Introduction
Pattern recognitions (PR) are defined as a perception task that perceives patterns in terms of
characteristic attributes, or features, and classifies these features into distinct classes [1]. A
fundamental pattern recognition system includes two principal processes: feature extraction and
classification. As we know, the purpose of feature extraction is to characterize attributes pattern
belonging to a class. If a complete set of discriminatory features for each pattern class can be found,
classification can be reduced to a simple matching process or a table look-up scheme. However, this
assumption is really too quixotic to be achieved in practical pattern recognition problems. Therefore,
only some, or the best discriminatory features are usually adopted. As for classification, its aim is
similar to that of feature extraction, which is to find the best class that is the closest to the classified
pattern. One of the issues that necessitate careful thought in a PR system is feature extraction and
selection. Feature selection entails the task to select a subset amongst a set of candidate features that
performs best under a classification system. This procedure can lessen not only the cost of recognition
by reducing the number of features that need to be collected, but in some cases it can also afford better
classification accuracy [2]. This is the typical PR framework. Conversely, the Principal Component
Analysis (PCA) is an eminent technique that has found great attentions in fields such as face
recognition [13], face pose classification [14], facial expressions [15], gesture recognition [16] and
detection of human activities [17]. However, the actual number of principal components or eigenvalues
to be retained in past work is vague. In this study, we deem further effort in selecting the best feature
vectors for the PR task based on the rules of thumb of PCA and performed Statistical Analysis prior to
classifications. In doing so, the most relevant component of the selected eigenvectors for classification
can be revealed. The structure of this paper includes materials and method in next section followed by
results and finally we conclude our findings.
Materials and Methods
System Overview
Figure 1 depicts an overview of the overall PR system that outlines the basic structure. To illustrate the
significance of our additional effort, we implement a PR task that handles a large database of human
posture images namely standing, sitting, bending and lying as illustrates in Figure 2. The preprocessing
stage consists of segmentation, feature extraction and feature selection. This phase extorts the
silhouette of a person which incorporate background subtraction followed by thresholding. Median
filtering and morphological operations are utilized for noise removal.