Sunday, February 17, 2008

Dynamic Gesture Recognition System for Koream Sign language

In this paper author has described a method based on proposed Fuzzy min-max neural networks. Considering the variety of possible gestures I the KSL, authors have reduced the number of possible gestures to 25 which according to them are the most common and basic gestures. The sensory information about the gestures is obtained by a data glove which generates 16 responses which is further reduced to obtain only the directional changes in the postures. Based on their data, they have reduced their data to frame 10-basic direction types which captures the directional change information in the postures. In order to reduce the data processing time and effective filtering, x and y range has been divided into 8 regions (based on their observation of the deviation in these directions) which form their local coordinate system. The directional information is stored in the 5 cascading registers. The directional information about each time unit is measured in these 5 cascading registers by “+” for the right/upper motion and “-“for the left lower motion and “x” is the no care position. Depending upon the previous position and the new position, measured through the values in these registers, change in direction is observed.

As per the author the 25 gestures have 14 postures which are recognized by the so called Fuzzy, min max neural networks. The Fuzzy min max neural network requires no pre learning about the postures and can be used for the online adaptability.

For recognition, the gestures generate the data which is inputted to the system which transforms this raw data set into asset containing small number of data which is then used to identify the direction class. After the direction class is recognized, posture recognition method is used to identify the gesture.

The complete system is represented by the figure shown below:


Discussion:

I did not have much to say about this paper because I have no clues what author wanted to convey by presenting the min max fuzzy neural network system which looks like a simple template matching system based on the direction segmentation of the postures. Except the direction values that have been used to classify the gestures, nothing is impressive. Also, they have considered 25 gestures with 14 postures which look simple with just finger movements (no yaw and pitch). Another flaw in the paper was that there is no mention of the user study for the paper as the min max values based on the data may be very user specific and the complete setup might require retuning with new data. As far as the results are considered, it looks very obscure to say about 85% as it is not clear if it is close to 85%, more or less. It would have been much better if they would have conducted various experiments and provided with some exact average classification. Also, it would have been nice if they would have spent some time explaining about their min-max NN which looks to me very confusing with the diagram presented.

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