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首页> 外文会议>IEEE International Conference on Image Processing >Makeup-insensitive face recognition by facial depth reconstruction and Gabor filter bank from women's real-world images
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Makeup-insensitive face recognition by facial depth reconstruction and Gabor filter bank from women's real-world images

机译：通过面部深度重构和Gabor滤波器库从女性真实世界图像中识别化妆不敏感的人脸

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In this paper, a new method was proposed to handle facial makeup in face recognition. To improve a face recognition method robust to facial makeup, features were extracted from facial depth in which facial makeup is not effective. Then, face depth features were added to face texture features to perform feature extraction. Accordingly, a 3D face was reconstructed from only a single 2D frontal image with/without facial expressions. Then, the texture and depth of the face were extracted from the reconstructed model. Afterwards, the Gabor Filter Bank (GFB) was applied to both texture and reconstructed depth of the face to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final feature vectors are generated and classified by the Support Vector Machine (SVM). Convincing results were achieved for makeup-insensitive face recognition on the available image database based on the present method compared to several state-of-the-art methods.
机译：本文提出了一种新方法来处理人脸识别中的人脸化妆。为了改进对脸部化妆有鲁棒性的脸部识别方法，从脸部深度中提取无效脸部特征的特征。然后，将面部深度特征添加到面部纹理特征以执行特征提取。因此，仅从具有/不具有面部表情的单个2D正面图像重建3D面部。然后，从重建的模型中提取面部的纹理和深度。然后，将Gabor滤波器组（GFB）应用于人脸的纹理和重建深度，以从纹理和重建的深度图像中提取特征向量。最后，通过组合2D和3D特征向量，由支持向量机（SVM）生成并分类最终特征向量。与几种最新方法相比，在基于本方法的可用图像数据库上对化妆不敏感的面部识别获得了令人信服的结果。

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