Pose and Illumination Invariant Face Recognition in Video

Yilei Xu, A. Roy-Chowdhury, Keyur Patel
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引用次数: 20

Abstract

The use of video sequences for face recognition has been relatively less studied than image-based approaches. In this paper, we present a framework for face recognition from video sequences that is robust to large changes in facial pose and lighting conditions. Our method is based on a recently obtained theoretical result that can integrate the effects of motion, lighting and shape in generating an image using a perspective camera. This result can be used to estimate the pose and illumination conditions for each frame of the probe sequence. Then, using a 3D face model, we synthesize images corresponding to the pose and illumination conditions estimated in the probe sequences. Similarity between the synthesized images and the probe video is computed by integrating over the entire sequence. The method can handle situations where the pose and lighting conditions in the training and testing data are completely disjoint.
视频中姿态和光照不变人脸识别
与基于图像的方法相比,使用视频序列进行人脸识别的研究相对较少。在本文中,我们提出了一个从视频序列中识别人脸的框架,该框架对面部姿势和光照条件的大变化具有鲁棒性。我们的方法是基于最近获得的理论结果,该结果可以将运动,照明和形状的效果集成在使用透视相机生成图像中。这个结果可以用来估计每一帧探针序列的姿态和照明条件。然后,利用三维人脸模型,合成与探测序列中估计的姿态和光照条件相对应的图像。通过对整个序列进行积分计算合成图像与探测视频之间的相似度。该方法可以处理训练数据和测试数据中姿态和光照条件完全不一致的情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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