Today, face recognition systems play a crucial role in many access control and automatic identification systems. However, these systems still have shortcomings that reduce their performance efficiency. In this paper, a novel face recognition framework is introduced, combining the Eigenfaces algorithm and image registration. Firstly, the collected face images are preprocessed, then the Eigenfaces algorithm is applied to them for obtaining the reference eigenvectors. After that, three test images are captured using a webcam, and the images' faces are detected using the Viola-jones algorithm. The detected faces are registered to the collected face images, and the detected face with the lowest mean square error is selected for subsequent steps. Next, the selected detected face's eigenvector and the distance between it and reference eigenvectors are calculated, respectively. The minimum distance is then compared with a manual threshold to recognize the person as an unknown or known person. If the person is recognized as a known person, the person's identity is identified as the person belongs to the minimum distance. For validating the presented method, a public and an exclusive face image database are used. The obtained results indicate that the proposed framework achieved a better performance than traditional similarity-based methods to recognize known and unknown persons and identify known persons.