Modified Adaboost method for efficient face detection

Face Detection using the Adaboost algorithm has been successfully used to detect faces in images. While the detection rate of the strong classifier trained by Adaboost is good, the false alarm rate of a single strong classifier is very high. Boosting the misclassified images during training increases the weights of the misclassified images with respect to the correctly classified images. Thus the subsequent weak classifiers are effectively trained using decreasing number of the input images. In this paper, a modification to the Adaboost method is proposed. Multiple strong classifiers based on different Haar-like feature types trained on the same set of input images are combined into a single modified-strong classifier. A comparison between the Adaboost method and the proposed method in terms of face non-face classification and face detection performance is provided. The proposed method demonstrates improved performance.