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Keypoint detection is one of the most important pre-processing steps in taskssuch as face modeling, recognition and verification. In this paper, we presentan iterative method for Keypoint Estimation and Pose prediction ofunconstrained faces by Learning Efficient H-CNN Regressors (KEPLER) foraddressing the face alignment problem. Recent state of the art methods haveshown improvements in face keypoint detection by employing Convolution NeuralNetworks (CNNs). Although a simple feed forward neural network can learn themapping between input and output spaces, it cannot learn the inherentstructural dependencies. We present a novel architecture called H-CNN(Heatmap-CNN) which captures structured global and local features and thusfavors accurate keypoint detecion. HCNN is jointly trained on the visibility,fiducials and 3D-pose of the face. As the iterations proceed, the errordecreases making the gradients small and thus requiring efficient training ofDCNNs to mitigate this. KEPLER performs global corrections in pose andfiducials for the first four iterations followed by local corrections in thesubsequent stage. As a by-product, KEPLER also provides 3D pose (pitch, yaw androll) of the face accurately. In this paper, we show that without using any 3Dinformation, KEPLER outperforms state of the art methods for alignment onchallenging datasets such as AFW and AFLW.

KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors