Hidden Markov Model-Based Gesture Recognition with Overlapping Hand-Head/Hand-Hand Estimated Using Kalman Filter

In this paper, we introduce a hand gesture recognition system to recognize isolated Malaysian Sign Language (MSL). The system consists of four modules: collection of input images, feature extraction, Hidden Markov Model (HMM) training, and gesture recognition. First, we apply skin segmentation proce...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2012 Third International Conference on Intelligent Systems Modelling and Simulation s. 262 - 267
Hlavní autoři: Gaus, Yona Falinie Abdul, Wong, Farrah
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.02.2012
Témata:
Equations
Feature extraction
gesture path
gesture trajectory
Handicapped aids
Hidden Markov Model
Hidden Markov models
Image color analysis
Kalman Filter
Mathematical model
Skin
skin segmentation
states
YCbCr
ISBN:1467308862, 9781467308861
ISSN:2166-0662
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, we introduce a hand gesture recognition system to recognize isolated Malaysian Sign Language (MSL). The system consists of four modules: collection of input images, feature extraction, Hidden Markov Model (HMM) training, and gesture recognition. First, we apply skin segmentation procedure throughout the input frames in order to detect only skin region. Then, we proceed to feature extraction process consisting of centroids, hand distance and hand orientation collecting. Kalman Filter is used to identify the overlapping hand-head or hand-hand region. After having extracted the feature vector, the hand gesture trajectory is represented by gesture path in order to reduce system complexity. We apply Hidden Markov Model (HMM) to recognize the input gesture. The gesture to be recognized is separately scored against different states of HMMs. The model with the highest score indicates the corresponding gesture. In the experiments, we have tested our system to recognize 112 MSL, and the recognition rate is about 83%.
ISBN:1467308862
9781467308861
ISSN:2166-0662
DOI:10.1109/ISMS.2012.67