Kinematic Constrained Cascaded Autoencoder for Real-Time Hand Pose Estimation
Hand pose estimation is an attractive problem in computer vision for its key role in gesture controlled humancomputer interaction (HCI) applications. This problem focuses on revealing the hand skeleton structure from visual information. However, it is very challenging for complicated hand configurations. In this paper, a kinematic constrained cascaded autoencoder regression (KCAE) framework is proposed to estimate the hand pose from a single depth image.We introduce a two-stage cascaded structure to regress the palm direction and the whole hand joints successively. In addition, the edge constraints are first introduced to the loss function with an endto- end manner, which maintains the kinematics of hands and makes the prediction more reasonable. With this framework, different features are evaluated, including the handcrafted features and the features learned from CNN. The experiments widely conducted on our collected dataset and the public MSRA hand gesture database demonstrate the effectiveness of KCAE. Overall, The proposed method achieves comparable performance with state-of-the-arts.