TY - GEN
T1 - Hand gesture recognition based on HOG-LBP feature
AU - Zhang, Fan
AU - Liu, Yue
AU - Zou, Chunyu
AU - Wang, Yongtian
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/10
Y1 - 2018/7/10
N2 - With the rapid development of information technology, human-computer interaction (HCI) is now experiencing the transition from traditional command line interface to novel natural user interface such as speech and gesture, thus vision-based hand gesture recognition is one of the key technologies to realize natural HCI. However, the performance of gesture recognition is often influenced by variations among lighting conditions, complex backgrounds and so on. This paper proposes a new fusion approach of hand gesture recognition by combining the HOG and uniform LBP feature on blocks, in which HOG features depict hand shape and LBP features depict hand texture. Support Vector Machine with radial basis function (RBF) as kernel function is adopted to train the hand gesture classifier. Experimental results show that HOG-LBP fused feature performs well on two sub-datasets from NUS hand posture dataset-II, reaching a relative high recognition accuracy of 97.8% and 95.07% respectively. The comparison experiments among HOG-LBP, HOG and LBP features also show that the HOG-LBP feature performs better than one single feature.
AB - With the rapid development of information technology, human-computer interaction (HCI) is now experiencing the transition from traditional command line interface to novel natural user interface such as speech and gesture, thus vision-based hand gesture recognition is one of the key technologies to realize natural HCI. However, the performance of gesture recognition is often influenced by variations among lighting conditions, complex backgrounds and so on. This paper proposes a new fusion approach of hand gesture recognition by combining the HOG and uniform LBP feature on blocks, in which HOG features depict hand shape and LBP features depict hand texture. Support Vector Machine with radial basis function (RBF) as kernel function is adopted to train the hand gesture classifier. Experimental results show that HOG-LBP fused feature performs well on two sub-datasets from NUS hand posture dataset-II, reaching a relative high recognition accuracy of 97.8% and 95.07% respectively. The comparison experiments among HOG-LBP, HOG and LBP features also show that the HOG-LBP feature performs better than one single feature.
KW - HOG-LBP feature
KW - SVM
KW - hand gesture recognition
KW - recognition accuracy
UR - http://www.scopus.com/inward/record.url?scp=85050773379&partnerID=8YFLogxK
U2 - 10.1109/I2MTC.2018.8409816
DO - 10.1109/I2MTC.2018.8409816
M3 - Conference contribution
AN - SCOPUS:85050773379
T3 - I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference: Discovering New Horizons in Instrumentation and Measurement, Proceedings
SP - 1
EP - 6
BT - I2MTC 2018 - 2018 IEEE International Instrumentation and Measurement Technology Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018
Y2 - 14 May 2018 through 17 May 2018
ER -