Pixel-In-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
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Recently, heatmap regression based models become popular because of their superior performance on locating facial landmarks. However, high-resolution feature maps have to be either generated repeatedly or maintained through the network for such models, which is computationally inefficient for practical applications. Moreover, their generalization capabilities across domains are rarely explored. To address these two problems, we propose Pixel-In-Pixel (PIP) Net for facial landmark detection. The proposed model is equipped with a novel detection head based on heatmap regression. Different from conventional heatmap regression, the new detection head conducts score prediction on low-resolution feature maps. To localize landmarks more precisely, it also conduct offset predictions within each heatmap pixel. By doing this, the inference time is largely reduced without losing accuracy. Besides, we also propose to leverage unlabeled images to improve the generalization capbility of our model through image translation based data distillation. Extensive experiments on four benchmarks show that PIP Net is comparable to state-of-the-arts while running at 27.8 FPS on a CPU.
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