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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Look More Into Occlusion: Realistic Face Frontalization and Recognition With BoostGAN
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Look More Into Occlusion: Realistic Face Frontalization and Recognition With BoostGAN

机译：看起来更闭塞：逼真的脸部正化和识别与boostgan

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摘要

Many factors can affect face recognition, such as occlusion, pose, aging, and illumination. First and foremost are occlusion and large-pose problems, which may even lead to more than 10% accuracy degradation. Recently, generative adversarial net (GAN) and its variants have been proved to be effective in processing pose and occlusion. For the former, pose-invariant feature representation and face frontalization based on GAN models have been studied to solve the pose variation problem. For the latter, frontal face completion on occlusions based on GAN models have also been presented, which is much concerned with facial structure and realistic pixel details rather than identity preservation. However, synthesizing and recognizing the occluded but profile faces is still an understudied problem. Therefore, in this article, to address this problem, we contribute an efficient but effective solution on how to synthesize and recognize faces with large-pose variations and simultaneously corrupted regions (e.g., nose and eyes). Specifically, we propose a boosting GAN (BoostGAN) for occluded but profile face frontalization, deocclusion, and recognition, which has two aspects: 1) with the assumption that face occlusion is incomplete and partial, multiple images with patch occlusion are fed into our model for knowledge boosting, i.e., identity and texture information and 2) a new aggregation structure integrated with a deep encoder–decoder network for coarse face synthesis and a boosting network for fine face generation is carefully designed. Exhaustive experiments on benchmark data sets with regular and irregular occlusions demonstrate that the proposed model not only shows clear photorealistic images but also presents powerful recognition performance over state-of-the-art GAN models for occlusive but profile face recognition in both the controlled and uncontrolled environments. To the best of our knowledge, this article proposes to solve face synthesis and recognition under poses and occlusions for the first time.
机译：许多因素会影响面部识别，例如闭塞，姿势，老化和照明。首先，最重要的是遮挡和大构成问题，甚至可能导致10％的精度下降。最近，已经证明生成的对抗性网（GaN）及其变体在加工姿势和闭塞方面是有效的。对于前者，已经研究了基于GaN模型的姿势不变特征表示和面部正压，以解决姿态变化问题。对于后者，还提出了基于GaN模型的闭塞的正面完成，这与面部结构和现实像素细节非常关注，而不是身份保存。然而，合成和识别闭塞但剖面面仍然是一个被解读的问题。因此，在本文中，为了解决这个问题，我们有助于有效但有效的解决方案如何合成和识别具有大姿势变化的面孔，并同时损坏区域（例如，鼻子和眼睛）。具体而言，我们提出了一个升压GaN（Boostagan），用于闭塞，但具有两个方面的剖面性，剖面面部的沉重化，取消沉积和识别，其中有两个方面：1）对于面部遮挡是不完全和部分的假设，具有贴片遮挡的多个图像被馈送到我们的模型中对于知识升级，即身份和纹理信息和2）仔细地设计了一种新的聚合结构，用于用于粗面合成的深度编码器 - 解码器网络和用于精细脸部生成的升压网络。具有常规和不规则遮挡的基准数据集的详尽实验表明，所提出的模型不仅显示出清晰的光电型图像，而且还针对最先进的GaN模型提供了强大的识别性能，用于遮挡，但在受控和不受控制的情况下环境。据我们所知，本文首次提出解决姿势和闭塞下的面部综合和识别。

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