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Multi-dropout regression for wide-angle landmark localization

We propose the Multi-Dropout Regression Network (MDRN) for real-time facial landmark localization across extreme poses. Different from most landmark localization methods only work for −45° to 45° in yaw, the proposed MDRN works for the full coverage of −90° to 90°. It employs the Single Shot Multibox Detector (SSD) [1] as a preprocessor for fast and accurate face detection. Given an SSD detected face, the MDRN locates the landmarks. The MDRN is composed of 2 double-layer convolution blocks, 1 triple-layer convolution block and 3 fully-connected layers. Unlike most networks with only one dropout layer connected to the last convolution layer, in the MDRN each convolution block is followed by a max-pooling layer and a dropout layer ahead of connecting to the next processing layer. Experiments reveal that multiple dropouts better stabilize the regression and improve the accuracy of landmark localization. To locate the landmarks on profile faces and other extreme poses, the MDRN is trained on an augmented database composed of imagery of synthesized poses. A comparison study shows that the proposed solution delivers a comparable performance to the state of the art for wide-angle landmark localization.