论文标题
用教师任务提取面部知识：姿势不变的面部识别的语义分割功能
Distilling Facial Knowledge With Teacher-Tasks: Semantic-Segmentation-Features For Pose-Invariant Face-Recognition
论文作者
论文摘要
本文展示了一种新的方法，可以使用语义分段特征提高面部识别姿势不变性。拟议的SEG-DISTILD-ID网络共同学习识别和语义分割任务，然后将分割任务“蒸馏”（Mobilenet编码）。在强调头置变化的公开数据集中，针对三个最先进的编码器对性能进行基准测试。实验评估表明，SEG-DISTILD-ID网络显示出明显的鲁棒性益处，相比之下，RESNET-101的测试准确性达到99.9％，VGG-19的96.1％和InceptionV3的96.1％。这是使用顶部编码器推理参数的大约十分之一来实现的。这些结果表明，蒸馏的语义细分特征可以有效地解决面部识别姿势不变。
This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation task is then "distilled" (MobileNet encoder). Performance is benchmarked against three state-of-the-art encoders on a publicly available data-set emphasizing head-pose variations. Experimental evaluations show the Seg-Distilled-ID network shows notable robustness benefits, achieving 99.9% test-accuracy in comparison to 81.6% on ResNet-101, 96.1% on VGG-19 and 96.3% on InceptionV3. This is achieved using approximately one-tenth of the top encoder's inference parameters. These results demonstrate distilling semantic-segmentation features can efficiently address face-recognition pose-invariance.
