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The Study on Face Detection Strategy Based on Deep Learning Mechanism

机译:基于深度学习机制的人脸检测策略研究

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In this paper, the deep learning based face detection strategy was proposed. The exploited detection framework has good tolerance to the slight shift of light amplitude and illumination angle. Furthermore, this framework is immune to the slight occlusion. In the detection framework, the CNN was utilized to extract the intrinsic feature and execute the classification on face image and non-face image by recursive convolution and down-sampling process. The convenience of this method lied on that it is not necessary to extract the features explicitly during the detection process. This strategy avoided the information absence due to the inappropriate feature selection. AS a contrast, the LBP-SVM-based feature extraction and classification strategy was utilized to execute the face detection task. The experiment showed the superiority of CNN on detection accuracy and robustness. The comparisons result showed the effectiveness of deep learning mechanism.
机译:本文提出了一种基于深度学习的人脸检测策略。利用的检测框架对光幅度和照明角度的轻微偏移具有良好的耐受性。此外,该框架不会受到轻微的咬合。在检测框架中,利用CNN提取内在特征,并通过递归卷积和下采样过程对人脸图像和非人脸图像进行分类。该方法的便利性在于,在检测过程中不必显式提取特征。该策略避免了由于不适当的特征选择而导致的信息缺失。作为对比,基于LBP-SVM的特征提取和分类策略被用于执行面部检测任务。实验证明了CNN在检测精度和鲁棒性方面的优越性。比较结果显示了深度学习机制的有效性。

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