针对人脸、指纹和手写签名等人员身份鉴定方法不能很好地满足煤矿井下人员管理系统需要的现状,基于局部保持映射(LPP)算法,提出一种监督LPP算法(SLPP),并应用于煤矿井下人员步态识别中。利用该方法对步态数据进行映射,得到步态数据在低维空间中的表示方法,再利用最近邻分类器对低维步态数据进行识别。在2个步态数据库中进行一系列步态识别试验,并与经典维数约简算法LDA、监督流形学习算法DLPP、判别映射嵌入(DPE)流形学习算法以及其他步态识别方法分别进行比较。试验结果表明,在同等试验条件下,SLPP识别率最高,从而验证了该算法的有效性和可行性。

Due to the humanDs face, fingerprint, handwritten signature and other personnel identification method could not be well to meet the requirements of the mine underground personnel management system, based on a locality preservation projection (LPP) algorithm, a supervision algorithm on the locality preserved projecti on (SLPP) was provided and was applied to the gait recognition of the mine underground personnel.The gait data could be projected with the supervision on locality pre served projection.The expression method of the gait data in the low dimension was obtained.The nearest neighbor classifier was applied to the identification of the low dimension gait data.A test of the series gait identification was conducted in two gait databases and was compared with the classic dimension reduction algorithm LDA, t he supervision manifold learning algorithm DLPP, discriminant projection embed manifold learning algorithm and other gait identification method.The test results showe d that under the same test conditions, the supervision on locality preserved projection (SLPP) would have the highest recognition rate and thus the validity and feasibilit y of the algorithm could be verified.

国家自然科学基金资助项目(60975005)；河南省教育厅科学技术研究重点资助项目(12B120012)；河南省重大科技攻关计划资助项目(122102210429)；