As one of the most visible applications in computer vision communication, Face Recognition (FR) has become significant role in the community. In the past decade, researchers have been devoting themselves to addressing the various problems emerging in practical FR applications in uncontrolled or less controlled environment. In many practical applications of FR (e.g., law enforcement, e-passport, ID card verification, etc.), there is only one sample per person. Face Recognition (FR) with a one sample per person is a very challenging problem due to the lack of information to predict the variations in the query image. The number of training samples per person will greatly affect the performance of face recognition.