Multiple face detection based on machine learning

Abstract

Facial detection has recently attracted increasing interest due to the multitude of applications that result from it. 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.