In this paper many face recognition algorithms and codes were studied and tested, and it was concluded that they still face the challenge of not providing optimal accuracy and precision, especially in the case of images that have some distortions such as those resulting from poor illumination, different angles of taking the image and different facial expressions or wear hats, masks or glasses. Although recognition technologies using iris and fingerprint are more accurate, face recognition technology is the most common and widely utilized since it is simple to apply and execute, in addition it can be used directly anywhere and does not require any physical input from user. The results show that the best performance of face recognition depends on the number of principal components (PCs), the percentage of face recognition increases in the ranges of 10%, 40%, 50%, 80%, 90% and 100% when the PCs increase in order of 1, 3, 5, 7, 11 and 15, respectively.
In this paper many face recognition algorithms and codes were studied and tested, and it was concluded that they still face the challenge of not providing optimal accuracy and precision, especially in the case of images that have some distortions such as those resulting from poor illumination, different angles of taking the image and different facial expressions or wear hats, masks or glasses. Although recognition technologies using iris and fingerprint are more accurate, face recognition technology is the most common and widely utilized since it is simple to apply and execute, in addition it can be used directly anywhere and does not require any physical input from user. The results show that the best performance of face recognition depends on the number of principal components (PCs), the percentage of face recognition increases in the ranges of 10%, 40%, 50%, 80%, 90% and 100% when the PCs increase in order of 1, 3, 5, 7, 11 and 15, respectively.