Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Scalability in ASL Recognition: Breaking Down Signs into Phonemes
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Real-Time Large Vocabulary Recognition System for Chinese Sign Language
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Glove-talkii: mapping hand gestures to speech using neural networks. an approach to building adaptive interfaces
Expand training set for face detection by GA re-sampling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Synthetic data generation technique in Signer-independent sign language recognition
Pattern Recognition Letters
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In Sign Language recognition, one of the problems is to collect enough data. Data collection for both training and testing is a laborious but necessary step. Almost all of the statistical methods used in Sign Language Recognition suffer from this problem. Inspired by the crossover and mutation of genetic algorithms, this paper presents a method to enlarge Chinese Sign language database through re-sampling from existing sign samples. Two initial samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the effectiveness of the proposed method, some experiments are carried out on a vocabulary with 350 static signs. Each sign has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for testing. The experimental results show that the data generated by the proposed method are effective.