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We propose the Edge-Embedded Multi-Dropout (EEMD) framework for real-time face alignment. The EEMD framework extracts facial edge features and explores multiple dropout architecture for locating facial landmarks. It consists of two major component networks, namely the Contour Detection Network (CDN) and the Multi-Dropout Network (MDN); and two supplementary networks, one for face detection and the other for pose regression. When a face is detected by the face detector, its pose will be classified by the pose classifier, then the associated facial edges be detected by the CDN, and then the landmarks be located by the MDN. The embedding of the CDN into the EEMD framework describes the observation that most landmarks are located on the contours/edges of the facial components and of the whole face. We revise a state-of-the-art edge detector as part of the base network for the CDN. The MDN is proposed to better design the regression architecture with appropriate dropout settings for better preventing overfitting and enhancing regression accuracy. Unlike most of the 2D approaches unable to locate landmarks in extreme poses, the proposed framework can detect landmarks on profile faces, i.e., ±90 • in yaw, in real time. Evaluated on benchmark databases, the EEMD demonstrates a competitive performance to other state-of-the-art approaches with a satisfying runtime speed.

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