In this paper, we propose a method to recognize faces from a set of consecutive video frames instead of a single image using super-resolution (SR). The SR process uses multiple frames acquired from video and combines information coming from them into a single image in higher resolution. As expected, a single low resolution image would contain less amount of information, than the same image taken from a video sequence with multiple other images with temporal changes from consecutive frames. the proposed method uses SR to generate a super resolved video sequences from a low resolution video sequences and uses frames acquired from the high resolution video sequences to train and test the performance of the principal component analysis based face recognition system. Entropy and MSE were used to check the performance of the system and the results showed the robustness of SR as a preprocessing step for recognition.