Facial alignment by using sparse initialization and random forest

Over the last decade, face alignment researches have been advancing rapidly and have vast applications related to face recognition, pose estimation, and human-robot interaction. Face alignment is typically performed in a two-stage fashion by alternatively using local landmark detector and global shape regularizer. While both landmark detector and shape regularizer have achieved impressive progress recently, shape initialization with mean shape remains a critical issue. In this paper, we present a unique sparse initialization method that is inspired by the sparse model. Experiment results with two datasets selected from the widely used Multi-PIE Database show that our method outperforms conventional initialization techniques. In addition to its capability to handle test data with high shape variability and potential occlusion, our method has merit of simplicity and can be easily integrated with other face alignment approaches.