Face Recognition System Using Independent Components Analysis and Support Vector Neural Network Classifier

With an increasing number of security threats in recent years, the field of automatic facial recognition has seen many new developments. The introduction of many new face recognition algorithms focuses on increasing the accuracy rate of the recognition system. This paper introduces a face recognition system using Independent Component Analysis (lCA) for feature extraction and a Support Vector Neural Network (SVNN) for classification. As well as introducing a comparison between SVNN and Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers, they are applied to prove the reliability of the proposed method. The implemented experiments use Yale databases, and the results prove that the proposed approach has a higher recognition rate than the (ICA+SVM) and (ICA+ANN) approaches for face recognition.