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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面部检测策略研究

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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:本文提出了基于深度学习的面部检测策略。利用检测框架具有良好的宽度宽度的光幅度和照明角度。此外,该骨架免受轻微闭塞的影响。在检测框架中,利用CNN来提取内在特征,并通过递归卷积和下采样过程对面部图像和非面部图像执行分类。该方法的便利性阐述了在检测过程中没有必要明确提取特征。由于特征选择不适当,该策略避免了信息缺席。作为对比度,利用基于LBP-SVM的特征提取和分类策略来执行面部检测任务。实验表明CNN在检测精度和鲁棒性上的优越性。比较结果表明了深度学习机制的有效性。

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