Ant colony optimization for feature selection in face recognition

To render the face recognition work more efficiently, ACOSVM, a face recognition system combining Ant Colony Optimization (ACO) with Support Vector Machine (SVM), is presented, which employs SVM classifier with the optimal features selected by ACO. The Principal Component Analysis method (PCA) is used to extract eigenfaces from images at the preprocessing stage, and then ACO for selection of the optimal subset features using cross-validation is described, which can be considered as wrapper approach in the feature selection algorithms. The experiments indicate that the proposed face recognition system with selected features is more practical and efficient when compared with others. And the results also suggest that it may find wide applications in the pattern recognition.