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首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >Multiple face detection based on machine learning
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Multiple face detection based on machine learning

机译：基于机器学习的多人脸检测

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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.
机译：由于面部检测的大量应用，面部检测最近引起了越来越多的兴趣。在这种情况下，我们使用了基于机器学习的方法，这些方法允许机器通过学习过程进行进化，并执行用传统算法无法或不可能完成的任务。根据这种情况，我们建立了四种方法（Haar-AdaBoost，LBP-AdaBoost，GF-SVM，GF-NN）之间的比较研究。这些技术根据提取数据的方式和采用的学习算法而有所不同。前两种方法“ Haar-AdaBoost，LBP-AdaBoost”基于Boosting算法，该算法既用于选择又用于学习具有级联分类的强分类器。最后两种分类方法“ GF-SVM，GF-NN”使用Gabor滤波器提取特征。从这项研究中，我们发现检测时间因一种方法而异。实际上，与其他方法相比，LBP-AdaBoost和Haar-AdaBoost方法是最快的。但是就检测率和错误检测率而言，Haar-AdaBoost方法仍然是这四种方法中的最佳方法。

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