Published Paper Details:

The multitude of applications of facial detection in ML has made it a topic of discussion. In this context, we have used methods based on machine learning that allows a machine to evolve through a learning process, and to perform tasks that are difficult or impossible to fill by more conventional algorithmic means. According to this context, we have established a comparative study between four methods (Haar-AdaBoost, LBP-AdaBoost, GF-SVM, GF-NN). These techniques vary according to the way in which they extract the data and the adopted learning algorithms. The first two methods "Haar-AdaBoost, LBP-AdaBoost" are based on the Boosting algorithm, which is used both for selection and for learning a strong classifier with a cascade classification. While the last two classification methods "GF-SVM, GF-NN" use the Gabor filter to extract the characteristics. From this study, we found that the detection time varies from one method to another. Indeed, the LBP-AdaBoost and Haar-AdaBoost methods are the fastest compared to others. But in terms of detection rate and false detection rate, the Haar-AdaBoost method remains the best of the four methods.