Face Detection Based On SVM

Statistical learning theory is a kind of new technology in machine learning field. Its main idea is to control learning machine's generalization ability by controlling its model complexity. Support vector machine(SVM) is a new kind of learning machine based on statistical learning theory, which has many advantages. It solves small-sample problems by using structural risk minimization(SRM) to take the place of empirical risk minimization(ERM). Moreover, nonlinear problems are changed into linear ones by using mapping the low dimension original space to high dimension feature space, and employing kernel function, which make the algorithm be realized easily. Because of such advantages, SVM becomes a hot spot of machine learning theory, and is applied successfully. As a new technology, there are also many shortcomings that need to be researched, such as the adaptive kernel function and parameter selection, the limitation in the scale of training set, the shortcomings of training methods, and the combination with the prior knowledge, etc. Therefore, there is a need to research further.Based on the combination of SVM and face detection, research about frontal face detection in color images is discussed in this paper. Furthermore, the selection of kernel function and kernel parameters optimization are emphases in this paper. The main research work is as follows: (1) The method to optimize kernel parameters based on genetic algorithm(GA) isproposed.Taking Gauss function for instance, the influence of parameter selection uponkernel performance is analyzed. Then the method to optimize kernel parameters based on genetic algorithm(GA) is proposed because GA is stronge in global optimization. The results of experiment show that the kernel parameters educed by the method improve the kernel performance, and the method consumes much less time than cross-validation. By using the method based on GA ,the performance of three kinds of kernel function are compared. Taking error rate as fitness, parameter optimization experiment of kernel Gauss, kernel cubic polynomial, and kernel quartic polynomial are conducted. By analyzing the obtained data, the results show that: kernel Gauss is robust in classification, but it has some drawbacks such as more support vectors, large computation, slow detection speed. Whereas kernel polynomial is on the contrary.(2) Applying genetic algorithm based kernel parameter optimization method to face detection, a hierarchical SVM classifier is provided and realized.Since kernel Gauss and kernel polynomial have their own advantages and disadvantages, it is good to combine them. Taking the amount of support vectors as fitness to optimize kernel quartic polynomial's parameter, then fast processing speed can be achieved. Taking error rate as fitness to optimize kernel Gauss's parameter, then accurate recognition can be achieved. First, applying kernel polynomial SVM to input image, several candidate face windows are obtained. Then these candidate face windows are fused, and the output of the fusion are applied to kernel Gauss SVM. Finally, the ultimate results can be obtained. And a skin detector removing background beforehand is a plus. The author believes that, the method of combining both efficiency and accuracy can be used to construct a robust face detecting system.