Dealing with Variability When Recognizing User’s Performance in Natural Gesture Interfaces
- Resource Type
- Authors
- Franck Multon; Richard Kulpa; Anthony Sorel; Emmanuel Badier
- Source
- Motion in Games ISBN: 9783642347092
MIG
- Subject
- Computer science
Gesture recognition
Speech recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Key (cryptography)
Hidden Markov model
Representation (mathematics)
Motion capture
Motion (physics)
Virtual actor
Gesture
- Language
Recognition of natural gestures is a key issue in videogames and other immersive applications. Whatever the motion capture device, the key problem is to recognize a motion that could be performed by different users at interactive time. Hidden Markov Models (HMM) are commonly used to recognize the performance of a user but they rely on a motion representation that strongly affects the global performance of the system. In this paper, we demonsrate that using a compact motion representation based on Morphology-Independent features offers better performance compared to classical motion representations especially for users whose data were not used for training.