基于HMM-FNN模型的复杂动态手势识别

Recognition of Complex Dynamic Gesture Based on HMM-FNN Model

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.

Recognition of complex dynamic gesture is a key issue for visual gesture-based human-computer interaction. In this paper, an HMM-FNN model is proposed for gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network for fuzzy rule modeling and fuzzy inference. Complex dynamic gesture has two important properties： Its motion can be decomposed and usually being defined in a fuzzy way. By HMM-FNN, complex gesture is firstly decomposed into three components： Posture changing, movement in 2D plane and movement in Z-axis direction, each of which is modeled by HMM. The likelihood of each HMM to observation sequence is considered as membership value of FNN, and gesture is classified through fuzzy inference of FNN. In this proposed method, high-dimensional gesture feature is transformed into several low-dimensional features, as a result, computational complexity is reduced. Furthermore, human＇s experience or prior knowledge can be used to build and optimize model structure. Experimental results show that the proposed method is an effective method for recognition of complex dynamic gesture, and is superior to conventional HMM method.