Eigenfaces and Support Vector Machine Approaches for Hybrid Face Recognition
نویسندگان
چکیده
Face Recognition has crucial effects in daily life especially for security purposes and their tasks are actively being used for many applications. In this study, we introduce a hybrid face recognition technique, consisting of two main parts namely feature extraction and classification. In the first part, as feature extracting techniques, we benefit from Eigenfaces method which is based on Principal Component Analysis (PCA). In the second part, after generating feature vectors, Support Vector Machines (SVMs) are utilized. We examined the classification accuracy according to three different SVM kernel types. For the test set, we focused on ORL face database including 400 images of 40 people. At the end of the overall recognition task, we have obtained the classification accuracy 91.2% with Radial Basis Function (RBF) kernel for 240 image training set.
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