A New Distance Metric Learning Algorithm for Hand Posture Recognition
- https://doi.org/10.2991/icmii-15.2015.43How to use a DOI?
- Distance metric learning. Nearest classify. Hand posture recognition.
A new distance metric learning algorithm for hand posture recognition is proposed in this paper. By adopting the Fourier descriptor of the hand shape as the sample feature, the algorithm exerts new constrains on the distance of inter-class pairs, and learns a Mahalanobis distance metric matrix by minimizing the distance of intra-class pairs and maximizing the distance of inter-class pairs simultaneously. Then k-nearest neighbor classifier is used to test the introduced method. The experimental results on multi-class hand posture recognition by k-nearest neighbor classifier show that the presented algorithm has less error rate than other distance metric learning algorithms.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qiongli Liu AU - Dajun Xu AU - Zhiguo Li AU - Peng Zhou AU - Jingjing Zhou AU - Yongxia Xu PY - 2015/10 DA - 2015/10 TI - A New Distance Metric Learning Algorithm for Hand Posture Recognition BT - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics PB - Atlantis Press SP - 232 EP - 236 SN - 2352-538X UR - https://doi.org/10.2991/icmii-15.2015.43 DO - https://doi.org/10.2991/icmii-15.2015.43 ID - Liu2015/10 ER -