Semi-supervised learning for facial component-landmark detection

Facial component and landmark detection have many applications in many facial analysis tasks. In this paper, a semisupervised method for this task is proposed to detect facial components and landmarks. Different from other facial detectors algorithms, our model without extra input solve the occlusion problem by detecting the visible facial components. Firstly, we propose a data augmentation method based on the Deep Convolutional Generative Adversarial Network to generate a large amount of semi-supervised training data. Then, a semi-supervised learning model based on Region-based CNN is responsible for multi-task facial component and landmark detection by training on the generated semi-supervised training data. During training, facial component regions and landmarks are used as supervised training data, while unsupervised training data only contains component bounding box. Experimental results illustrate that the proposed model can handle multi-task facial detection, and outperforms the state-of-the-art algorithms.