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Published in:
2016 | OriginalPaper | Chapter
Gesture Spotting by Using Vector Distance of Self-organizing Map
Authors : Yuta Ichikawa, Shuji Tashiro, Hidetaka Ito, Hiroomi Hikawa
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Abstract
This paper proposes a dynamic hand gesture recognition algorithm with a function of gesture spotting. The algorithm consists of two self-organizing maps (SOMs) and a Hebb learning network. Feature vectors are extracted from input images, and these are fed to one of the SOMs and a vector that represents the sequence of postures in the given frame is generated. Using this vector, gesture classification is performed using another SOM. In the SOM, the vector distance between the input vector and the winner neuron’s weight vector is used for the gesture spotting. The following Hebb network identifies the gesture class. The experimental results show that the system recognizes eight gestures with the accuracy of 95.8 %.