Face Recognition Using the Basic Components Analysis Algorithm

Abstract

Since all the things surrounding us have characteristics, therefore, of course, there are several characteristics and features of the human face that distinguish it and know it from others, making it a distinct organism with certain features that can be classified and identified on its basis. Since the detection and cutting of faces from the image is a critical problem that has gained importance in recent times, they play a major role in facial recognition systems. In this research, we present a new method for identifying faces using the Principal Component Analysis PCA algorithm, through the passage of the image in several stages, starting with the stage of obtaining the image (taking it); then, the face detection phase of the original image, and aligning the image (i.e. adjusting the face angle to the camera angle). After that, we enter the image with the stage of extracting the important basic features of the image; then, we match the required image with the available image store. The proposed algorithm was tested with several images and faces and faces without faces were successfully recognized. The proposed algorithm is characterized by a high efficiency in the detected faces. The accuracy of this algorithm is more than 95% in the detection faces. The proposed algorithm is a prerequisite for any system that uses the face as the main feature.