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首页> 外文会议>International conference on communications and cyber physical engineering >Deep Learning Based Representation for Face Recognition
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Deep Learning Based Representation for Face Recognition

机译：基于深度学习的人脸识别表示

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摘要

Face Recognition is one of the challenging process due to huge amount of wild datasets. Deep learning has been provided good solution in terms of recognition performance, as day by day this have been dominating the field of biometric. In this paper our goal is to study deep learning based face representation under several different conditions like lower and upper face occlusions, misalignment, different angles of head poses, changing illuminations, flawed facial feature localization using deep learning approaches. For extraction of face representation two different popular models of Deep learning based called Lightened CNN and VGG-Face and have reflected in this paper. As both of this model show that deep learning model is robust to different types of misalignment and can tolerate localizations error of the intraocular distance.
机译：由于存在大量野生数据集，因此人脸识别是具有挑战性的过程之一。深度学习已在识别性能方面提供了很好的解决方案，因为它已日益主导生物识别领域。在本文中，我们的目标是研究使用深度学习方法在几种不同条件下进行基于深度学习的面部表示，例如上下面部遮挡，未对准，头部姿势的不同角度，照明变化，有缺陷的面部特征定位。为了提取人脸表征，本文反映了两种不同的基于深度学习的流行模型，即Lightened CNN和VGG-Face。正如这两个模型都表明，深度学习模型对于不同类型的未对准具有鲁棒性，并且可以容忍眼内距离的定位误差。

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