Key frame based multi-level classification of sign language recognition
基于关键帧的多级分类手语识别研究*

This paper presented a sign language recognition method based on the multi-level classification of key frame recognition. This method adopted hierarchical discriminant regression (HDR) and dynamic time warping (DTW) template to match multi-level classification. According to the multi-frame characteristic of sign language, adopted the scale-invariant feature transform (SIFT) algorithm to orient and obtain the key frames of sign language vocabularies, and extracted the feature vectors. Based on these key frames of sign language vocabularies, the adopted HDR method could narrow the search scope. Then used the DTW compare the irrecognition features of sign language vocabularies with every sign language word inside this scope, and the maximal calculate probability was the recognition result. With the same recognition rate, this method could be 8.2% faster than the HMM recognition method, and solved the problem that the template matching was suddenly slow down in the face of a large vocabulary.