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Pooling Facial Segments to Face: The Shallow and Deep Ends

机译:汇集面部段面对面:浅层和深度

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Generic face detection algorithms do not perform very well in the mobile domain due to significant presence of occluded and partially visible faces. One promising technique to handle the challenge of partial faces is to design face detectors based on facial segments. In this paper two such face detectors namely, SegFaee and DeepSegFaee, are proposed that detect the presence of a face given arbitrary combinations of certain face segments. Both methods use proposals from facial segments as input that are found using weak boosted classifiers. SegFace is a shallow and fast algorithm using traditional features, tailored for situations where real time constraints must be satisfied. On the other hand, DeepSegFace is a more powerful algorithm based on a deep convolutional neutral network (DCNN) architecture. DeepSegFace offers certain advantages over other DCNN-based face detectors as it requires relatively small amount of data to train by utilizing a novel data augmentation scheme and is very robust to occlusion by design. Extensive experiments show the superiority of the proposed methods, specially DeepSegFace, over other state-of-the-art face detectors in terms of precision-recall and ROC curve on two mobile face datasets.
机译:由于遮挡和部分可见面的显着存在,通用面检测算法在移动域中不会在移动域中进行非常好。一种处理部分面部面临挑战的承诺技术是基于面部段设计面部探测器。在本文中,提出了两个这样的面部探测器,即SegFaee和DeepsegFAEE,以检测某些面部段的任意组合的面部的存在。这两种方法都使用来自面部段的提案作为使用弱升压分类器找到的输入。 SEGFACE是一种使用传统功能的浅且快速的算法,适用于必须满足实时限制的情况。另一方面,DeepSeGFace是一种基于深度卷积中立网络(DCNN)架构的更强大的算法。 DeepSegface通过利用新颖的数据增强方案,在其他基于DCNN的面部探测器上提供了某些优点,因为它需要采用新型数据增强方案来培训,并且非常坚固地通过设计闭塞。广泛的实验表明,在两个移动脸部数据集上的精确召回和ROC曲线方面,在其他最先进的面部探测器上,拟议方法的优越性。

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