Face Detection Using a Modified SVM-Based Classifier
Majid Roohi, Ghasem Mirjalily, Mohammad Taghi Sadeghi · 2007
The Support Vector Machine (SVM) classifier is among the most successful methods for faces detection. In this classifier, an optimal hyperplane is determined as the decision boundary in order to determine face or non-face regions. An important issue in the SVM classifier is to shift the decision level adequately towards the better represented class. In this paper, a novel method is proposed for determining the shift value adaptively. A post processing algorithm is also presented for reducing the false alarm rate. Experimental results show that the performance of the proposed SVM-based method is much better than the basic SVM classifier.