Fast Sign Language Recognition Benefited From Low Rank Approximation

This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the low rank approximation of the original sign videos for two aspects. First, under the observations that most visual information of a sign sequence typically concentrates on limited key frames, we apply an online low rank approximation of sign videos for the first time to select the key frames. Second, rather than fixing the number of hidden states for large vocabulary of variant signs, we further take the advantage of the low rank approximation to independently determine it for each sign to optimise predictions. With the key frame selection and the variant number of hidden states determination, an advanced framework based on HMMs for Sign Language Recognition (SLR) is proposed, which is denoted as Light-HMMs (because of the fewer frames and proper estimated hidden states). With the Kinect sensor, RGB-D data is fully investigated for the feature representation. In each frame, we adopt Skeleton Pair feature to character the motion and extract the Histograms of Oriented Gradients as the feature of the hand posture appearance. The proposed framework achieves an efficient computing and even better correct rate in classification. The widely experiments are conducted on large vocabulary sign datasets with up to 1000 classes of signs and the encouraging results are obtained.