论文标题
使用可变形的对称Gabor小波网络的小嘈杂和透视面部检测
Small Noisy and Perspective Face Detection using Deformable Symmetric Gabor Wavelet Network
论文作者
论文摘要
低分辨率图像中的面部检测和跟踪并不是一项琐碎的任务，因为面部表征的外观特征的限制。此外，面部表情会在这张小而嘈杂的脸上造成额外的失真。在本文中，我们提出了可变形的对称Gabor小波网络面部模型，用于低分辨率图像中的面部检测。我们的模型优化了具有对称约束的面部模型的旋转，翻译，扩张，透视和部分变形量。对称约束有助于我们的模型对噪声和失真更加强大。我们低分辨率的面部图像数据集和视频的实验结果在各种挑战性的条件下显示出有希望的面部检测和跟踪结果。
Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization. Moreover, facial expression gives additional distortion on this small and noisy face. In this paper, we propose deformable symmetric Gabor wavelet network face model for face detection in low resolution image. Our model optimizes the rotation, translation, dilation, perspective and partial deformation amount of the face model with symmetry constraints. Symmetry constraints help our model to be more robust to noise and distortion. Experimental results on our low resolution face image dataset and videos show promising face detection and tracking results under various challenging conditions.
