A Detailed Look At CNN-based Approaches In Facial Landmark ...

Computer Science > Computer Vision and Pattern Recognition arXiv:2005.08649 (cs) [Submitted on 8 May 2020] Title:A Detailed Look At CNN-based Approaches In Facial Landmark Detection Authors:Chih-Fan Hsu, Chia-Ching Lin, Ting-Yang Hung, Chin-Laung Lei, Kuan-Ta Chen View a PDF of the paper titled A Detailed Look At CNN-based Approaches In Facial Landmark Detection, by Chih-Fan Hsu and 3 other authors View PDF

Abstract:Facial landmark detection has been studied over decades. Numerous neural network (NN)-based approaches have been proposed for detecting landmarks, especially the convolutional neural network (CNN)-based approaches. In general, CNN-based approaches can be divided into regression and heatmap approaches. However, no research systematically studies the characteristics of different approaches. In this paper, we investigate both CNN-based approaches, generalize their advantages and disadvantages, and introduce a variation of the heatmap approach, a pixel-wise classification (PWC) model. To the best of our knowledge, using the PWC model to detect facial landmarks have not been comprehensively studied. We further design a hybrid loss function and a discrimination network for strengthening the landmarks' interrelationship implied in the PWC model to improve the detection accuracy without modifying the original model architecture. Six common facial landmark datasets, AFW, Helen, LFPW, 300-W, IBUG, and COFW are adopted to train or evaluate our model. A comprehensive evaluation is conducted and the result shows that the proposed model outperforms other models in all tested datasets.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as:	arXiv:2005.08649 [cs.CV]
(or arXiv:2005.08649v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2005.08649 Focus to learn more arXiv-issued DOI via DataCite

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From: Chih-Fan Hsu [view email] [v1] Fri, 8 May 2020 16:17:42 UTC (4,827 KB) Full-text links:

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