Using Principal Component Analysis And Hidden Markov Model For Hand Recognition Systems

摘要

There are many approaches and algorithms that can be used to recognize and synthesize the hands gesture. Each approach has its own advantages and characteristics. This paper describes the usage of Hidden Markov Models (HMM) and Principal Component Analysis (PCA) in recognizing hands gesture by two different researches. The limitations of each techniques and comparisons between each other will be detailed below.