基于自适应子空间在线PCA的手势识别

基于视觉的手势识别系统的学习一般是离线的，导致系统对新手势的正确识别需要重新离线学习，因此系统实时性、可扩展性和鲁棒性较差，不适合认知发育的智能框架。文中提出了基于自适应子空间在线PCA的手势识别方法。该方法通过计算样本投影系数向量的PCA来实现子空间在线更新，并根据新样本与已学习样本的差异程度，调整子空间更新策略，使算法自适应于不同情况，减少计算和存储开销，实现增量的在线学习和识别手势的目的。实验表明，本文方法能处理未知手势问题，实现手势在线积累和更新，逐渐增强系统识别能力。

Hand Gesture Recognition Based on Online PCA with Adaptive Subspace

The learning method for hand gesture recognition system based on vision is commonly off-line, which results in repeated off-line learning when new hand gestures come. Its real-time performance, expansibility and robustness are poor. In this paper, a method named online principle component analysis (PCA) with adaptive subspace is proposed for hand gesture recognition. The subspace is updated online by calculating PCA of sample coefficients. The subspace updating strategy is adjusted according to the degree of difference between new sample and learned sample. The algorithm is able to adapt to different situations and reduce the cost of calculation and storage. The incremental online learning and recognition of hand gestures are realized by the proposed algorithm. Experimental results show that the proposed method solves the unknown hand gesture problem, realizes online hand gesture accumulation and updating and improves the recognition performance of system.