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Recently, Deep Convolution Networks (DCNNs) have been applied to the task offace alignment and have shown potential for learning improved featurerepresentations. Although deeper layers can capture abstract concepts likepose, it is difficult to capture the geometric relationships among thekeypoints in DCNNs. In this paper, we propose a novel convolution-deconvolutionnetwork for facial keypoint detection. Our model predicts the 2D locations ofthe keypoints and their individual visibility along with 3D head pose, whileexploiting the spatial relationships among different keypoints. Different fromexisting approaches of modeling these relationships, we propose learnabletransform functions which captures the relationships between keypoints atfeature level. However, due to extensive variations in pose, not all of theserelationships act at once, and hence we propose, a pose-based routing functionwhich implicitly models the active relationships. Both transform functions andthe routing function are implemented through convolutions in a multi-taskframework. Our approach presents a single-shot keypoint detection method,making it different from many existing cascade regression-based methods. Wealso show that learning these relationships significantly improve the accuracyof keypoint detections for in-the-wild face images from challenging datasetssuch as AFW and AFLW.

A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection