PERFORMANCE EVALUATION OF FEATURE EXTRACTION TECHNIQUES USING APPEARANCE BASED ALGORITHMS
Abstract
Digital image processing is the use of computer algorithms to analyze digital images. Digital image processing, involves many processing stages of which feature extraction stage is important. Feature extraction involves reducing the number of resources required to describe a large set of data. However, choosing a feature extraction techniques is a problem because of their deficiencies and different abilities. Thus, this paper presents a comprehensive research on the performance evaluation of feature extraction techniques, with focus centered on adopting appearance based algorithms . The Labeled Faces in the Wild dataset was adopted for the research and the face images were first pre-processed and then subjected to selected feature extraction techniques (Principal Component Analysis, Gabor Filters, Independent Component Analysis and Linear Discriminant Analysis). The extracted features were then classified using a Support Vector Machine (SVM) classifier.
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