Optimized Method for Real-Time Face Recognition System Based on PCA and Multiclass Support Vector Machine

Automatic face recognition system is one of the core technologies in computer vision, machine learning, and biometrics. The present study presents a novel and improved way for face recognition. In the suggested approach, first, the place of face is extracted from the original image and then is sent to feature extraction stage, which is based on Principal Component Analysis (PCA) technique. In the previous procedures which were established on PCA technique, the whole picture was taken as a vector feature, then among these features, key features were extracted with use of PCA algorithm, revealing finally some poor efficiency. Thus, in the recommended approach underlying the current investigation, first the areas of face features are extracted; then, the areas are combined and are regarded as vector features. Ultimately, its key features are extracted with use of PCA algorithm. Taken together, after extracting the features, for face recognition and classification, Multiclass Support Vector Machine (SVMs) classifiers, which are typical of high efficiency, have been employed. In the result part, the proposed approach is applied on FEI database and the accuracy rate achieved 98.45%.