1. A system for recognizing a disguised face, the system comprising: a graph generation means configured to generate a single standard face graph from a plurality of facial image samples; a support vector machine (SVM) learning means configured to determine an optimal classification plane for discriminating a disguised face from the plurality of facial image samples and disguised facial image samples, wherein the SVM learning means generates an initial face graph by adjusting a size of the single standard face graph based on position points of both eyes within a rectangular facial area, and generates an optimal face graph by comparing a similarity between a Gabor feature value at each node of the initial face graph and a standard Gabor feature value at the each node of the single standard face graph and repeatedly modifying the initial face graph using a particle swarm optimization (PSO) algorithm based on a result of the comparison; wherein the PSO algorithm is an evolutionary calculation used to obtain an optimal solution from a complex function by exchanging information with a personal particle and a particle within a swarm, using variable parameters such as a center between both eyes, a size scaling parameter of the entire single standard face graph, an upper size scaling parameter of the both eyes, or a lower size scaling parameter of the both eyes; and wherein the personal particle is a first data point from the single standard face graph, and the particle within the swarm is a second data point from the swarm, and the swarm is a collection of data points; a facial recognition means configured to determine whether an input facial image is disguised using the standard face graph and the optimal classification plane, when the facial image to be recognized is input.