This paper presents the recognition system for the Japanese sign language. In this paper authors have highlighted that sometimes the superfluous gestures are included in the recognition results which is not desirable. As such we need to segment these superfluous gestures, which are actually the caused by in correct segmentation because of incorrect identification of the border of the signed words.Also they intend to identify if the gesture is made by two hands or one hand.
In order to identify the borders they have proposed two measures. One is the measure of change of velocity and the other is the measure of the change in direction and hand movements dynamically. The segmentation point is registered if the measure exceeds certain threshold. Since the measure of the hand movement can lead to the different borders because of noise, they proposed using the hand border as one which is closest to the border detected by the change in velocity border. Using this information, gestures can be segmented from the stream of gestures and sent to the recognition system for identification.
In order to identify the hand which is used in the gesture, they calculate several parameters representing the difference between the movements of the right and the left hand. These measures are basically the velocity ratio relative to each other and the difference of the velocity squared value normalized with time of the gesture.These parameters are calculated separately for the left and the right hand and if the value is less than certain threshold for bot hands then both hands are used else one hand is being used.They have used another measure which is based on just the relative velocity of the hand to determine which hand is used in the gesture.
The sequence candidates are generated by evaluating a measure, which takes into consideration if the identified segment is the transition or a word, and only considering the words. The segments identified as words are combined with the segments identified as transitions using a weighted sum and this is used for a sentence.
They evaluated thus system using 100 samples from JSL which included 575 word segments and 571 transition segments out of which 46`(80.2%) of transitions were correctly recognized and 64(11.2%) were misjudged as words.
Discussion:
This paper presented an approach which was similar to what we have been using in sketch i.e taking cues from the speed and direction about the stroke. I am not sure if we can use the velocity cues with much accuracy as normally the gestures are made very fast, however I liked that they have also used the change in hand movements too and then used both of the cues to identify the border. However, I did not like the flow of the paper and I was confused with the language too. It was not very clear how they got the thresholds and if people with different styles and speed can get use the system with same thresholds. Also they have admitted that some of the gestures were mis identified as their system does not take any spatial information which may the error in many gestures.
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2 comments:
I was again disappointed by yet another paper that did not show what gestures they were using. Second, I did not like the fact that they only used the kinematics data. I won't even begin to discuss the lack of user data... Not so good of a paper...
For a very early paper, relative to what we've read so far, I thought it was not bad. I guess that demonstrates how there hasn't been much progress in the field since this paper was published. I too was greatly disturbed by its sparse evaluation info, which handicapped this paper from being a commonly cited paper to only an occasionally cited one.
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