Face recognition systems perform accurately in a controlled environment, but an unconstrained environment dramatically degrades their performance. In this study, a novel pose-invariant face recognition system is proposed based on the occlusion free regions. This method utilises a gallery set of frontal face images and can handle large pose variations. For a 2D probe face image with an arbitrary pose, the head pose is first obtained using a robust head pose estimation method. Then, this 2D face image is normalised by a novel 3D modelling method from a single input image. In consequence, pose invariant face recognition is converted to a frontal face recognition problem. The 3D structure is reconstructed using a new method based on the estimated head pose and only one facial feature point, which is significantly reduced in comparison with the number of landmarks used in previous methods. According to the estimated poses, occlusion free regions are extracted from normalised images as feature extraction. Finally, face matching and recognition is performed using these regions from normalised test images and the corresponding regions of gallery images. Experimental results on FERET and CAS-PEAL-R1 databases demonstrate that the proposed method outperforms other methods, and it is robust and efficient.

中文翻译：

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基于匹配3D通用模型对齐的无遮挡区域的姿势不变人脸识别

人脸识别系统可在受控环境中准确执行，但不受约束的环境会严重降低其性能。在这项研究中，基于无遮挡区域的人脸识别系统被提出。这种方法利用了一组正面图像库，可以处理较大的姿势变化。对于具有任意姿势的2D探针面部图像，首先使用鲁棒的头部姿势估计方法获得头部姿势。然后，通过新颖的3D建模方法从单个输入图像对2D面部图像进行归一化。结果，姿势不变的面部识别被转换为正面面部识别问题。根据估算的头部姿势和仅一个面部特征点，使用一种新方法重建了3D结构，与以前方法中使用的界标数量相比，该数量明显减少。根据估计的姿势，从归一化图像中提取无遮挡区域作为特征提取。最后，使用来自标准化测试图像的这些区域和图库图像的相应区域来执行面部匹配和识别。在FERET和CAS-PEAL-R1数据库上的实验结果表明，该方法优于其他方法，并且鲁棒有效。