State-of-the-art methods of attribute detection from faces almost always assume the presence of a full, unoccluded face. Hence, their performance degrades for partially visible and occluded faces. In this paper, we introduce SPLITFACE, a deep convolutional neural network-based method that is explicitly designed to perform attribute detection in partially occluded faces. Taking several facial segments and the full face as input, the proposed method takes a data driven approach to determine which attributes are localized in which facial segments. The unique architecture of the network allows each attribute to be predicted by multiple segments, which permits the implementation of committee machine techniques for combining local and global decisions to boost performance. With access to segment-based predictions, SPLITFACE can predict well those attributes which are localized in the visible parts of the face, without having to rely on the presence of the whole face. We use the CelebA and LFWA facial attribute datasets for standard evaluations. We also modify both datasets, to occlude the faces, so that we can evaluate the performance of attribute detection algorithms on partial faces. Our evaluation shows that SPLITFACE significantly outperforms other recent methods especially for partial faces.

中文翻译：

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基于分段的局部人脸属性检测方法

最先进的人脸属性检测方法几乎总是假设存在完整的、未被遮挡的人脸。因此，对于部分可见和被遮挡的人脸，它们的性能会下降。在本文中，我们介绍了 SPLITFACE，这是一种基于深度卷积神经网络的方法，明确设计用于在部分遮挡的人脸中执行属性检测。以几个面部片段和全脸作为输入，所提出的方法采用数据驱动的方法来确定哪些属性定位在哪些面部片段中。网络的独特架构允许多个段预测每个属性，这允许实施委员会机器技术以结合本地和全局决策以提高性能。通过访问基于细分的预测，SPLITFACE 可以很好地预测那些定位在人脸可见部分的属性，而不必依赖整个人脸的存在。我们使用 CelebA 和 LFWA 面部属性数据集进行标准评估。我们还修改了两个数据集，以遮挡人脸，以便我们可以评估属性检测算法在部分人脸上的性能。我们的评估表明，SPLITFACE 明显优于其他最近的方法，尤其是对于局部人脸。