This document discusses using sparse representation for face recognition systems. It introduces face recognition as a pattern analysis problem involving identifying people from images or video. The key idea is to use sparse representation to represent face images in terms of a over-complete dictionary of facial features like the eyes, nose, and mouth. The algorithm works by finding the sparsest representation of a query face over the dictionary and classifying it based on which representation has the minimum error. References are provided on sparse representation techniques for computer vision and pattern recognition applications, including face recognition.