基于最佳特征子集的自适应非视距身份识别系统

ADAPTIVE NON-LINE-OF-SIGHT IDENTIFICATION SYSTEM BASED ON BEST FEATURE SUBSET

身份识别一直是安防领域的研究重点,其在非视距场景下的研究存在较大意义。针对识别的舒适度和隐私性问题,提出基于最佳特征子集的自适应非视距身份识别系统。通过有效结合多种预处理手段获取Wi-Fi信号的低维有用数据;提出鲁棒性人员检测方法截取有效片段;设计有监督特征提取方法,使用“前向搜索”获取最佳特征子集;改进传统Adaboost算法实现群体变化下的自适应识别。实验评估表明,当系统中志愿者为2~12人时,与相关系统和传统分类算法相比,均具有较好的性能。

Identification has always been the focus of research in the field of security,and its research in non-line-of-sight scenarios is of great significance.Aimed at comfort and privacy of recognition,a best feature subset based adaptive non-line-of-sight identification system is proposed.Low-dimensional useful data of Wi-Fi signals was obtained by effectively combining multiple preprocessing methods.A robust human detection method was proposed to intercept effective fragments.A supervised feature extraction method was designed,and"forward search"was employed to obtain the best feature subset.A traditional Adaboost algorithm was improved to realize adaptive recognition under group variation.Experimental evaluation shows that when the number of volunteers in system is 2~12,which has better performance compared with related systems and traditional classification algorithms.