基于能量图分解与运动偏移特性的步态识别

提出一个新的步态识别算法.首先通过计算轮廓图距离来检测异常步态轮廓图,并利用平均近邻图与平均轮廓图重建异常的图像.然后将对象的步态能量图分解为两部分,并分别为每一部分生成一系列扩展图像,从而构造出能量分解图.接着根据主干图与步态偏移图的对应关系消除静态形状信息,并进行脚部区域校正,从而构造出运动偏移图.最后,使用能量分解图和运动偏移图共同进行分类.实验结果表明,本文算法的识别率远远高于3个典型算法.     

一种基于主曲线的步态识别方法

提出将主曲线作为一种新的步态特征分析和分类方法.主曲线特征分析单独分析每类样本的特征,形成直接对各类样本特征及其趋势的低维流形描述,保留了数据集的内在拓扑结构.首先对步态序列时空分析,在低的代价下表达步态运动的时空变化模式;然后,对步态特征进行主曲线分析;最后,用针对该分析方法定义的新相似性度量和分类规则进行了步态的训练和识别.在常用数据库上的测试结果表明,本方法行之有效,主曲线具有很好的实用性.     

基于步态的身份识别

步态就是个体的行走方式，它作为一种适用于非接触式远距离身份识别的生物特征近来倍受关注。文章提出了一种简单有效的利用行人的步态特征识别身份的算法。对于每个序列而言，行人的重心偏移量首先被用来去除序列中的冗余图像帧；然后，从图像中提取二值化人体轮廓的宽度信息，通过对其进行特征空间变换和规范空间变换来获得可分类的低维步态特征；标准的模式分类技术用于最终的识别。     

Gait recognition using radon transform and linear discriminant analysis.

A new feature extraction process is proposed for gait representation and recognition. The new system is based on the Radon transform of binary silhouettes. For each gait sequence, the transformed silhouettes are used for the computation of a template. The set of all templates is subsequently subjected to linear discriminant analysis and subspace projection. In this manner, each gait sequence is described using a low-dimensional feature vector consisting of selected Radon template coefficients. Given a test feature vector, gait recognition and verification is achieved by appropriately comparing it to feature vectors in a reference gait database. By using the new system on the Gait Challenge database, very considerable improvements in recognition performance are seen in comparison to state-of-the-art methods for gait recognition.     

Gait recognition based on vision systems: A systematic survey

With the growing popularity of biometrics technology in the pattern recognition field, especially identification of human has gained the attention of researchers from both academia and industry. One such type of biometric technique is Gait recognition, which is used to identify a human being based on their walking style. Generally, two types of approaches are adopted by any algorithm designed for gait recognition, namely model based and model free approaches. The key reason behind the popularity of gait recognition is that it can identify a person from a considerable distance while other biometrics has failed to do so. In this paper, the authors have conducted a survey of extant studies on gait recognition in consideration of gait recognition approaches and phases of a gait cycle. Moreover, some aspects like floor sensors, accelerometer based recognition, the influences of environmental factors, which are ignored by exiting surveys, are also covered in our survey study. The information of gait is usually obtained from different parts of silhouettes. This paper also describes different benchmark datasets for gait recognition. This study will provide firsthand knowledge to the researchers working on the gait recognition domain in any real-world field. It has been observed that work done on the gait recognition with sufficiently high accuracy is limited in comparison to research on various other biometric recognition systems and has enough potential for future research.     

Gait Components and Their Application to Gender Recognition

Human gait is a promising biometrics resource. In this paper, the information about gait is obtained from the motions of the different parts of the silhouette. The human silhouette is segmented into seven components, namely head, arm, trunk, thigh, front-leg, back-leg, and feet. The leg silhouettes for the front-leg and the back-leg are considered separately because, during walking, the left leg and the right leg are in front or at the back by turns. Each of the seven components and a number of combinations of the components are then studied with regard to two useful applications: human identification (ID) recognition and gender recognition. More than 500 different experiments on human ID and gender recognition are carried out under a wide range of circumstances. The effectiveness of the seven human gait components for ID and gender recognition is analyzed.     

用于步态识别的多层窗口图像矩

该文提出了一种以局部性矩统计量作为步态特征描述的步态识别方法.首先提取行人二值轮廓序列,构造一种基于直方图的轮廓点分布特征检测出步态周期;然后生成彩色步态运动历史图像CGHI描述步态的空间特征和时间信息;继而设计了多层同心矩形窗口分割CGHI,提取出一组矩形环窗口的矩特征量作为步态特征,在此基础上实现了步态识别.在Soton数据库上进行了实验,提出算法的正确识别率可达87.2%,优于现有方法.     

Gait probability image: An information-theoretic model of gait representation

In this paper, we propose a new probabilistic gait representation to characterize human walking for recognition by gait. The approach obtains the binomial distribution of every pixel in a gait cycle. Organizing the binomial distribution of all pixels in the gait image, we obtain the gait signature, which we denote as the Gait Probability Image (GPI). In the recognition stage, symmetric Kullback–Leibler divergence is used to measure the information theoretical distance between gait signatures. The experimental results reveal that GPI achieves promising recognition rates. Besides that, experiments on different walking speeds demonstrate that GPI is robust to slight variation in walking speed.