Current Mood: 
studious
Blogs I Commented On:
Summary:
This is a hand gesture recognition paper which uses a neural networks-based approach for the domain of Taiwanese Sign Language (TSL). Data for 20 right-hand gestures was retrieved using a VICON, which are then fed into their neural network, where 15 geometric distances were employed as feature representation of the different gestures. Their backpropagation neural network structure, implemented in MATLAB, had 15 input units, 20 output units, and two hidden layers in total. Recognition rates for varying number of neurons gave roughly 90% accuracy.
Discussion:
This is the second GR paper which used NNs, and I think that this paper was superior simply because their NN was more advanced. It was a vanilla NN implementation though, and it seemed like they just got some data and ran it though some MATLAB NN library. It’s a very straightforward paper that would have been more interesting had their gestures been more representative or complex. A vast majority of the gestures in this paper derive from words which are hardly used in the language. It appears that the authors preferred to work with gestures that were easier to classify as opposed to gestures that were actually common used.
1 comments:
Easy to classify > commonly used in almost all the papers we've seen.
Their discussion of the NN differences was nice, although they should have focused a bit more on the gestures themselves instead of the layers of the NN.
Post a Comment