We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hybrid Approach Based on Machine Learning for Hand Shape and Key Point's Estimation.
- Authors
Chen, Zhongshan; Feng, Xinning; Martínez, Oscar Sanjuán; Crespo, Rubén González
- Abstract
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.
- Subjects
FIX-point estimation; MACHINE learning; HUMAN-computer interaction; HUMAN fingerprints
- Publication
Journal of Interconnection Networks, 2022, Vol 22, p1
- ISSN
0219-2659
- Publication type
Article
- DOI
10.1142/S0219265921410218