System for recognizing disguised face using gabor feature and SVM classifier and method thereof

A system for recognizing a disguised face, the system comprising:

• a detector configured to detect a rectangular facial area from an input facial image using an Adaboost algorithm;
• a normalization unit configured to normalize the detected rectangular facial area to a predetermined size;
• an extractor configured to generate an optimal face graph using the normalized rectangular facial area, and to extract a Gabor feature value and a position value from the generated optimal face graph,
• wherein the generating an optimal face graph includes comparing a similarity between the Gabor feature value at each node of the initial face graph and a standard Gabor feature value at the each node of the standard face graph and repeatedly modifying the initial face graph using a particle swarm optimization (PSO) algorithm based on a result of the comparison, and
• 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 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;
• and
• a determining unit configured to determine whether the input facial image is disguised using the extracted Gabor feature value and the position value, and a pre-generated optimal classification plane for recognizing a disguised face, wherein the determining unit determines the input facial image by adjusting a size of the standard face graph based on position points of the both eyes within a the rectangular facial area.

A method of recognizing a disguised face, the method comprising:

• generating, using a processor, a single standard face graph from a plurality of facial image samples;
• generating, using the processor, an optimal classification plane for recognizing the disguised face from the plurality of facial image samples and disguised facial image samples, wherein the generating of the optimal classification plane 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 standard face graph and repeatedly modifying the initial face graph using a particle swarm optimization (PSO) algorithm based on a result of the comparison, and
• 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 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;
• determining, using the processor, 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.

A method of recognizing a disguised face, the method comprising:

• detecting a rectangular facial area from an input facial image using an Adaboost algorithm;
• normalizing the detected rectangular facial area to a predetermined size;
• generating an optimal face graph using the normalized rectangular facial area, and extracting a Gabor feature value and a position value from the generated optimal face graph;
• and
• determining whether the input facial image is disguised using the extracted Gabor feature value and the position value, and a pre-generated optimal classification plane for recognizing the disguised face, wherein generating includes generating an initial face graph by adjusting a size of a standard face graph based on position points of both eyes within the rectangular facial area, and generates an the optimal face graph by comparing a similarity between the Gabor feature value at each node of the initial face graph and a standard Gabor feature value at the each node of the 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 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.