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LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Cross-Annotation Face Alignment

ICLR 2022(2022)

Univ British Columbia | Univ Queensland | PhD student | Southern Univ Sci & Technol

Cited 1|Views17
Abstract
We innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks. Due to the embedding of LDDMM into a deep network, LDDMM-Face can consistently annotate facial landmarks without ambiguity and flexibly handle various annotation schemes, and can even predict dense annotations from sparse ones. Our method can be easily integrated into various face alignment networks. We extensively evaluate LDDMM-Face on four benchmark datasets: 300W, WFLW, HELEN and COFW-68. LDDMM-Face is comparable or superior to state-of-the-art methods for traditional within-dataset and same-annotation settings, but truly distinguishes itself with outstanding performance when dealing with weakly-supervised learning (partial-to-full), challenging cases (e.g., occluded faces), and different training and prediction datasets. In addition, LDDMM-Face shows promising results on the most challenging task of predicting across datasets with different annotation schemes.
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Face alignment,Facial landmarks,Diffeomorphic mapping,Deep learning
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要点】:本文创新性地提出了一种灵活且一致性的面部对齐框架LDDMM-Face,通过引入形变层将面部几何自然嵌入到微分同胚的方式中,实现了对弱监督学习下面部标志的准确预测。

方法】:LDDMM-Face通过预测独特参数化形变的矩,以微分同胚注册的方式代替传统的热图或坐标回归预测面部标志,实现面部边界的精确定位。

实验】:本文在300W、WFLW、HELEN和COFW-68四个基准数据集上对LDDMM-Face进行了广泛评估,结果显示,LDDMM-Face在传统数据集内部及相同标注设置下与现有先进方法相当或更优,尤其在处理弱监督学习、具有挑战性的案例(如遮挡面部)以及不同训练和预测数据集间的面部对齐任务中表现突出。