Modeling and Recognition of Gesture Signals in 2D Space: A Comparison of NN and SVM Approaches

Authors:
Farhad Dadgostar;Abdolhossein Sarrafzadeh;Chao Fan;Liyanage De Silva;Chris Messom
Affiliations:
Institute of Information and Mathematical Sciences, New Zealand;Institute of Information and Mathematical Sciences, New Zealand;Institute of Information and Mathematical Sciences, New Zealand;Massey University, New Zealand;Institute of Information and Mathematical Sciences, New Zealand
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

Citing 0
Cited 1

On the Selection of a Classification Technique for the Representation and Recognition of Dynamic Gestures

IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence

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Abstract

In this paper we introduce a novel technique for modeling and recognizing gesture signals in 2D space. This technique is based on measuring the direction of the gradient of the movement trajectory as features of the gesture signal. Each gesture signal is represented as a time series of gradient angle values. These features are classified by applying a given classification method. In this article we compared the accuracy of a feed forward Artificial Neural Network with a Support Vector Machine using a radial kernel. The comparison was based on the recorded data of 13 gesture signals as training and testing data. The average accuracy of the ANN and SVM were 98.27% and 96.34% respectively. The false detection ratio was 3.83% for ANN and 8.45% for SVM, which suggests the ANN is more suitable for gesture signal recognition.