  Gesture recognition is a challenging problem in the field of biometrics. In
this paper, we integrate Fisher criterion into Bidirectional Long-Short Term
Memory (BLSTM) network and Bidirectional Gated Recurrent Unit (BGRU),thus
leading to two new deep models termed as F-BLSTM and F-BGRU. BothFisher
discriminative deep models can effectively classify the gesture based on
analyzing the acceleration and angular velocity data of the human gestures.
Moreover, we collect a large Mobile Gesture Database (MGD) based on the
accelerations and angular velocities containing 5547 sequences of 12 gestures.
Extensive experiments are conducted to validate the superior performance of the
proposed networks as compared to the state-of-the-art BLSTM and BGRU on MGD
database and two benchmark databases (i.e. BUAA mobile gesture and SmartWatch
gesture).
