Hybrid Approach Based on Machine Learning for Hand Shape and Key Point’s Estimation

In human-computer interaction and virtual truth, hand pose estimation is essential. Public dataset experimental analysis Different biometric shows that a particular system creates low manual estimation errors and has a more significant opportunity for new hand pose estimation activity. Due to the fluctuations, self-occlusion, and specific modulations, the structure of hand photographs is quite tricky. Hence, this paper proposes a Hybrid approach based on machine learning (HABoML) to enhance the current competitiveness, performance experience, experimental hand shape, and key point estimation analysis. In terms of strengthening the ability to make better self-occlusion adjustments and special handshake and poses estimations, the machine learning algorithm is combined with a hybrid approach. The experiment results helped define a set of follow-up experiments for the proposed systems in this field, which had a high efficiency and performance level. The HABoML strategy decreased analysis precision by 9.33% and is a better solution.

InterNet+: A Light Network for Hand Pose Estimation

Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement. Based on the architecture of MobileNet v3 and MoGA, we redesigned a feature extractor that introduced the latest achievements in the field of computer vision, such as the ACON activation function and the new attention mechanism module, etc. Using these modules effectively with our network, architecture can better extract global features from an RGB image of the hand, leading to a greater performance improvement compared to InterNet and other similar networks.