Research of Face Detection Classifier Based on Support Vector Machine
Abstract
Face Detection is a process that the face of all including the location, size, and configuration is identified when the image is input, In an image environment, the face’s color, position, facial expressions, and other aspects are so diversity and sometimes there will be some shielding that the detection algorithm becomes so complex, and also make many problems in the computation. To address the problem, many researchers propose a variety of solutions, including Support Vector Machine (SVM) which can solve some actual problems such as the small sample, nonlinear, and high-dimensional, and now many researchers pay attention to it.
The traditional classification on face detection includes the face patterns matching method and the PCA method, and so on. There are a lot of deficiency in the face of the pass rate、inhuman face filtration rate and the computational complexity.
Support Vector Machine (SVM) is based on structural risk minimization principle of statistical learning methods. linear and nonlinear SVM classifier can be get ideal face detection classification results, but the nonlinear SVM method have a lack of higher computational complexity.
In order to improve face detection’s performance, and reducing the complexity and the detection time, a SVM-based Face Detection System is proposed. This system support vector machine can raise the checking speed and lower the number of false alarm effectively by using the appropriate face detection algorithm.
This algorithm first locate face and do filtration roughly by using the "eyes - the average face template ", and then finish the face detection through a rough classification of SVM linear and nonlinear detection SVM classifier.
Key words: Face Detection , Support Vector Machine ,Classifier , Linear
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