TY - GEN
T1 - Comparison of Recognition Accuracy on Dynamic Hand Gesture Using Feature Selection
AU - Sooai, Adri Gabriel
AU - Batarius, Patrisius
AU - Siki, Yovinia Carmeneja Hoar
AU - Nani, Paskalis Andrianus
AU - Mamulak, Natalia Magdalena Rafu
AU - Ngaga, Emerensiana
AU - Rosiani, Ulla Delfana
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
AU - Mau, Sisilia Daeng Bakka
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Dynamic Hand Gesture Recognition is carried out in various studies to read patterns. Various sensors can be used to capture dynamic hand movement patterns. The results of the initial reading are usually in the form of raw data that must pass initial processing. Advanced processing is carried out to obtain features that will be trained using various classifiers. The recognition process without feature selection activities will reduce the accuracy of pattern recognition during the classification process. Seeing the many shortcomings in the implementation of the initial data processing, this study will present some initial processing examples to produce features that are relatively good for the data training process. The method used is the Gaussian mixture model and the selection of predictors for the classification process. The sensor for recording dynamic hand movements used in this study is Leap-motion. There are three dynamic hand gestures were used in this study. The data used were 4609 coordinates spread in 30 features. The classifiers used are k-NN with Euclidean distance metric without feature selection process compare to the process with feature selection. The results obtained from this study are the availability of examples of feature selection models in the form of Gaussian mixtures and some accuracy of the results of processing comparison. The lowest prediction, without and with feature prediction, has a slightly range from 99.7% to 100%.
AB - Dynamic Hand Gesture Recognition is carried out in various studies to read patterns. Various sensors can be used to capture dynamic hand movement patterns. The results of the initial reading are usually in the form of raw data that must pass initial processing. Advanced processing is carried out to obtain features that will be trained using various classifiers. The recognition process without feature selection activities will reduce the accuracy of pattern recognition during the classification process. Seeing the many shortcomings in the implementation of the initial data processing, this study will present some initial processing examples to produce features that are relatively good for the data training process. The method used is the Gaussian mixture model and the selection of predictors for the classification process. The sensor for recording dynamic hand movements used in this study is Leap-motion. There are three dynamic hand gestures were used in this study. The data used were 4609 coordinates spread in 30 features. The classifiers used are k-NN with Euclidean distance metric without feature selection process compare to the process with feature selection. The results obtained from this study are the availability of examples of feature selection models in the form of Gaussian mixtures and some accuracy of the results of processing comparison. The lowest prediction, without and with feature prediction, has a slightly range from 99.7% to 100%.
KW - Distance Metric
KW - Dynamic Hand Gesture
KW - Feature Selection
KW - Gaussian Mixture Model
KW - Recognition Accuracy
UR - http://www.scopus.com/inward/record.url?scp=85066483172&partnerID=8YFLogxK
U2 - 10.1109/CENIM.2018.8711397
DO - 10.1109/CENIM.2018.8711397
M3 - Conference contribution
AN - SCOPUS:85066483172
T3 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
SP - 270
EP - 274
BT - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Y2 - 26 November 2018 through 27 November 2018
ER -