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Comparative Graph Model for Facial Recognition Systems

Template-Based face recognition methods have some limitation for the key points which are used for detection based on the common areas in facial images. One disadvantage of using a template-based method is that not all valuable key points are used. Nodes that are outside the areas that are sought with a template will be overlooked. Some of these key points can provide a more accurate description of the face. Using a method that compares training images against each other rather than a template helps to incorporate all key points of interest for a particular person. By comparing all images against each other, to seek similarity based on distances and repetitive nodes, a node map can be built. This is the foundation for the Comparative Graph Model (CGM). By adjusting a required threshold for repetition and allowable distance among nodes, the algorithm can build a node map solely based on similarity of images across training data.