基于移动终端嵌入式的视觉传感网络的人像识别技术  

Portrait Recognition Technology Based on Mobile Terminal Embedded Visual Sensing Network

作　　者：陈亮亮[1] 李达辉[1] 

机构地区：[1]琼州学院电子信息工程学院,海南三亚572022

出　　处：《科技通报》2015年第8期219-221,共3页Bulletin of Science and Technology

基　　金：海南省自然科学基金(613170)

摘　　要：针对视觉传感网络的人像识别过程中容易出现移动设备使用的光照、背景易发生改变,使用者姿态、眼睛、表情等发生变化的问题,提出一种基于移动终端嵌入式的视觉传感网络的人像识别技术,寻找每个样本点的若干个近邻点,通过各样本点的近邻点求出该样本点的局部重建权值矩阵,从而求出该样本点的输出值。对视觉传感网络人像灰度值的均值以及方差进行灰度归一化处理,使其相等。根据视觉传感网络人像特征矢量样本获取两个最佳鉴别矢量,将其作为基底矢量转换成子空间,多次输入视觉传感器网络的人像,获取该类模式的人像特征矢量样本集,给出正态模式下的Bayes分类模型,获取单元型最小距离分类器。仿真实验结果表明,所提方法具有很高的人像识别精度。As recognition for visual sensor networks prone to mobile devices using light, in the process of background changes, user profile, eyes, facial expression change, such as problems, put forward a kind of mobile terminal based on embedded vision sensing network recognition technology of the portrait, looking for a number of each sample point is the neighbor points, through the sample points of neighbor points out local reconstruction weights matrix of the sample points, thus the output value of the sample points. Of visual sensor networks like grey value of the mean and variance of gray scale normal- ization processing, so that it is equal. Root based on the visual sensing network as characteristic vector samples for obtain- ing the best identify two vectors, as a basal vector into subspace, portrait, multiple input visual sensor network access to the class as characteristic vector of pattern sample set, given normal mode of the Bayes classification model, the minimum distance classifier for unit type. The simulation results show that the proposed method has the very high as the identification accuracy.

关 键 词：移动终端 嵌入式 人像识别 

分 类 号：TN911[电子电信—通信与信息系统]