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基于DCNN的人脸多属性识别

An Improved Algorithm of the Screen Space Ambient Occlusion
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摘要 在人脸属性的识别过程中,目前常见的方法有以下几种,基于Gabor小波变换的人脸属性识别,基于SIFT的人脸属性识别和基于差分纹理的人脸属性识别。传统方法存在很多问题,例如特征的选取需要人为的干预,而且特征的选择也不一定能够符合预期。采用有监督的基于深度卷积神经网络(DCNN)的方法,构建一个多层卷积神经网络,通过卷积神经网络获得深度卷积激活特征,该方法采用CelebA库训练,之后用JAFFE人脸库进行检测,取得很好的实验结果。 In computer graphics, the quality of global illumination directly affects the authenticity of the frames. The traditional ray tracing is com-plex, and difficult to online. So in real-time applications such as games generally choose the Ambient Occlusion, AO technology to simu-late global illumination effect, which sacrifice some effect to reach real-time rendering.
作者 广长彪
出处 《现代计算机(中旬刊)》 2017年第3期51-54,76,共5页 Modern Computer
基金 科技部重大仪器专项(No.2013YQ49087904)
关键词 属性识别 SIFT 监督 深度卷积神经网络 Global Illumination Ambient Occlusion Screen Space Mipmap
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