An i-vector based descriptor for alphabetical gesture recognition

An i-vector approach to extracting features for video camera based gesture recognition is proposed. Conventional low-level raw features, such as position, speed, and acceleration, are low-dimensional feature representations which often suffer from measurement noise and thus are not highly discriminative. High-level features, such as Fourier descriptor, usually take a global transformation on the whole raw features of a gesture, but local statistical information is seldom considered. Moreover, compared with speech recordings, video cameras used to capture data are often at a low frame rate such that it is challenging for proper modeling and recognition. In this paper, we show that the proposed i-vector framework can handle both local statistical information and sparse trajectory representations more efficiently under the sparse data scenarios for an in-car hand-gesturing English letter recognition system. Experimental results confirm the effectiveness of the proposed i-vector features, which can reduce the letter error rate by as much as 36-44% relatively from the results obtained with the conventional location based raw features.