Face recognition is a very important issue and can be used in many applications. In such application, matching is an important process since the image source cannot take two identical forms. Hence the image may be changed by different types of motion which may be applied to it. Also face recognition is an important application in image matching technique. It is largely needed for surveillance and security, telecommunication, digital libraries, human-computer intelligent interaction, and smart environments. Principle Component Analysis (PCA) is a good way of compressing and identifying patterns in data, and where the need for reliable recognition and identification of interacting users is obvious. This thesis describes two schemes of face recognition in the presence of motion based on Principal Component Analysis (PCA) and Wavelet Decomposition. The first proposed scheme exploits feature extraction capabilities of the Discrete Wavelet Transform Decomposition and invokes certain normalization techniques that increase its robustness to variation in facial geometry and illumination. A second scheme is proposed to improve the performance of the first method which employed a hybrid transform through the combination of Fourier, Radon and Wavelet transforms. The first method gives a success recognition rate of 80%. This performance has been improved to 95.2% when the second method is used for a database of 140 images. The proposed two methods are tested using MATLAB® program version 7 and the simulation results agreed well with the theoretical ones.