Multi-class AdaBoost learning of facial feature selection through grid computing

AdaBoost is an efficient method for producing a highly accurate learning algorithm by assembling multiple classifiers, but it is also widely known for its long duration of off-line learning. Especially, when it is applied for feature selection for object detection, its learning process is to exhaustively evaluate every feature in a large set. With the increasing of image resolution and complexity of feature transformation approaches, the computational time will be extremely long, which makes the large scale AdaBoost learning very difficult. In this paper, we have employed Grid Computing to solve the difficulty. The proposed algorithm is to select the most significant features for face recognition. The selection algorithm is derived from multi-class AdaBoost, which exhaustively evaluate every feature from a large set. The deployed Grid Computing system is actually used for High Throughput Computing specialised on advanced resource management. To utilizing Grid Computing on the feature selection process, we have improved multi-class AdaBoost learning algorithm with parallel structure, so that the task of High Performance Computing is accomplished in the environment of High Throughput Computing. With Grid Computing, selecting 200 features from a large set of 30240 features is finished in 20 days, while without Grid Computing the time would be more than two years. It shows that Grid Computing brings vast advantage to computer vision, machine learning, image processing, and pattern recognition.