The recognition rate of these systems is a function in the image resolution. The results show that increasing the image resolution using the mentioned interpolation methods improves the performance of the recognition systems considerably. The first part of this research demonstrates the impact of the image resolution on the performance of the face recognition system. For the classification, this research uses k-nearest neighbor (k-NN) and Extreme Learning Machine-based neural network (ELM)? The performance of several holistic face recognition algorithms is evaluated for low-resolution face images. In the second part of this research, nearest neighbor, bilinear, and bicubic interpolation techniques are applies as a preprocessing step to increase the resolution of the input image to obtain better results. In a wide range of face recognition applications, such as the surveillance camera in law enforcement, it is cannot provide enough resolution of face for recognition.