Facial Face Recognition Based On High-Performance Topographical Features Selected Using Adaptive Feature Selection Method - Arif Sameh Arif (International Journal of Advanced Science and Technology, 2020)
In this paper, a new features selection method is represented to enhance a feature type that is alreadyadopted in previous classification researches. By studying successive differences between image values offace image, a relatively huge set of topographical features (TGF) are extracted from assigning each valueimage pixel to the related feature. An Efficient Feature Selection for face recognition (EFS) method isproposed to study and analyze TGF features. Such method used standard measures used to determinefeature performance adopted in standard methods, in addition to the major concept of Principal ComponentAnalysis (PCA) in dimension reduction for determining efficient features. EFS analyzed the dispersion ofyielded values for each feature within images of the dataset in order to determine the performance level ofeach feature. Eventually, the level of determined performance is assigned to the related features which aresorted in descending manner. Based on a controlled threshold, the highest set of performance values, whichare the best expected set of features, are selected as candidate features for Support Vector Machine (SVM)classifier. Collecting proposed features and the selection method yielded encouraging results up to(94.12%) as classification accuracy with considerable differences comparing with the State of Art.