Adaboost face detector based on Joint Integral Histogram and Genetic Algorithms for feature extraction process

Recently, many classes of objects can be efficiently detected by the way of machine learning techniques. In practice, boosting techniques are among the most widely used machine learning for various reasons. This is mainly due to low false positive rate of the cascade structure offering the possibility to be trained by different classes of object. However, it is especially used for face detection since it is the most popular sub-problem within object detection. The challenges of Adaboost based face detector include the selection of the most relevant features from a large feature set which are considered as weak classifiers. In many scenarios, however, selection of features based on lowering classification errors leads to computation complexity and excess of memory use. In this work, we propose a new method to train an effective detector by discarding redundant weak classifiers while achieving the pre-determined learning objective. To achieve this, on the one hand, we modify AdaBoost training so that the feature selection process is not based any more on the weak learner's training error. This is by incorporating the Genetic Algorithm (GA) on the training process. On the other hand, we make use of the Joint Integral Histogram in order to extract more powerful features. Experimental performance on human faces show that our proposed method requires smaller number of weak classifiers than the conventional learning algorithm, resulting in higher learning and faster classification rates. So, our method outperforms significantly state-of-the-art cascade methods in terms of detection rate and false positive rate and especially in reducing the number of weak classifiers per stage.