Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking

• URL: http://arxiv.org/abs/2004.02108v7
• Date: Thu, 27 Aug 2020 13:54:22 GMT
• Title: Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking
• Authors: Shi Yin, Shangfei Wang, Xiaoping Chen, Enhong Chen
• Abstract summary: We propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two groups of 1D heatmaps to represent the marginal distributions of the x and y coordinates. Second, a co-attention mechanism is adopted to model the inherent spatial patterns existing in x and y coordinates. Third, based on the 1D heatmap structures, we propose a facial landmark detector capturing spatial patterns for landmark detection on an image.
• Score: 73.35078496883125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although heatmap regression is considered a state-of-the-art method to locate facial landmarks, it suffers from huge spatial complexity and is prone to quantization error. To address this, we propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two groups of 1D heatmaps to represent the marginal distributions of the x and y coordinates. These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates. With much lower spatial complexity, the proposed method can output high-resolution 1D heatmaps despite limited GPU memory, significantly alleviating the quantization error. Second, a co-attention mechanism is adopted to model the inherent spatial patterns existing in x and y coordinates, and therefore the joint distributions on the x and y axes are also captured. Third, based on the 1D heatmap structures, we propose a facial landmark detector capturing spatial patterns for landmark detection on an image; and a tracker further capturing temporal patterns with a temporal refinement mechanism for landmark tracking. Experimental results on four benchmark databases demonstrate the superiority of our method.

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