Reading 2# Specifying Gestures by Example (Rubine)

Comments:
chris aikens
Summary:
The paper introduced a gesture recognition system called GRANDMA, Gesture Recognizer Automated in a Novel Direct Manipulation Architecture.

Firstly, it introduced the design of GRANDMA, such as how to create new gestures, how to delete gestures, how to edit gestures' semantics, etc. It made us aware of how GRANDMA worked.

Secondly, it described the principle of the recognizer. Gestures were recognized by a linear classifier with 13 features including angle, bounding rectangle,etc. The decision was made by finding the maximal value of similarity. "Rejection" was introduced to prevent ambiguous results. The recognizer can run in real time with high accuracy.

Finally, extensions were discussed, such as eager recognition, multi-finger recognition.

Discussion:
In general, GRANDMA should be a milestone in Rubine's time. An automated system  with high accuracy but little runtime was a great work in the field of gesture recognition. And it was able to get high accuracy in several datasets.

Recognizer: Accuracy rate decreases by increase of the gesture class.  Overlearning will occur due to the linear classifier. Linear classifier is fast but limited to the number of class. The classifier only spends less than 20ms to recognize. Maybe the classifier should be more complex to include more classes.

Eager Recognition: Eager Recognition makes GRANDMA more intelligent, and makes people more satisfied with GRANDMA. However, no free lunch. Eager Recognition limits the scope of gesture class to some extent.