非特定人手语识别问题中的合成数据驱动方法

Synthesized Data Driving: An Approach Toward Signer-Independent Sign Language Recognition

针对手语识别研究中训练样本缺乏,提出了一种衍生数据的方法,有效地解决了动态多数据流手语训练用样本合成问题.利用mean-shift算法可以方便、快捷地得到密度函数梯度的变化方向,从而控制衍生的方向和强度.算法同时考虑到合成样本尽可能包含非特定人的信息及其有效性,对数据所实现的变形不会被识别系统的初始化过程逆转.合成数据驱动的效果受模型的容量、合成的强度与方向影响.在多种实验环境下对驱动效果进行评估,识别率有所提高,在某些例子中提高明显.

The lack of training samples is an imperative problem in the research of sign language recognition. In this paper, a method of using derivative data is proposed, which facilitates synthesizing dynamic samples of multi-stream employed in sign language training. With the mean-shift algorithm, the movement direction of density function grads can be obtained easily, thus controling the direction and the intensity of derivation. At the same time, this algorithm can also satisfy the need for the synthesized samples to include as much and as effective information of unspecific signer as possible. Moreover, the method realizes a transformation of data which is irrevocable in the initialization process of the recognition system. The driving effect of the synthesized data depends on the capacity of the model, as well as the intensity and direction of synthesization. After assessing the driving effect under various experiment environments, it is found that in most cases the recognition rate is raised; and in some cases, it is even markedly raised.