Gait flow image: A silhouette-based gait representation for human identification

In this paper, we propose a novel gait representation—gait flow image (GFI) for use in gait recognition. This representation will further improve recognition rates. The basis of GFI is the binary silhouette sequence. GFI is generated by using an optical flow field without constructing any model. The performance of the proposed representation was evaluated and compared with the other representations, such as gait energy image (GEI), experimentally on the USF data set. The USF data set is a public data set in which the image sequences were captured outdoors. The experimental results show that the proposed representation is efficient for human identification. The average recognition rate of GFI is better than that of the other representations in direct matching and dimensional reduction approaches. In the direct matching approach, GFI achieved an average identification rate 42.83%, which is better than GEI by 3.75%. In the dimensional reduction approach, GFI achieved an average identification rate 43.08%, which is better than GEI by 1.5%. The experimental result showed that GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.     

正面视角的步态识别

现有的步态识别算法研究几乎全都是基于侧面步态的.提出一种基于正面视角的步态识别方法.首先归一化RGB颜色空间被用来检测和去除阴影,并用背景减除法提取二值化人体轮廓.提出一种专门适用于正面步态的周期检测方法,提取周期关键帧后跟踪轮廓线,并用改进的等角度采样法进行采样以减少计算量.简单高效的傅里叶描述子被用来提取特征向量,进行数据降维后构造步态模板.用最近邻和最近邻标本分类器分别进行分类.在CASIA数据库上的实验表明,该算法不仅具有较低的计算量而且表现出较好的识别性能.     

HNPG步态特征数据库管理系统设计与实现

为增强步态识别算法的评估和验证的可靠性,加速步态识别技术的实际应用进程,针对大量非结构化数据管理问题和当前小样本步态数据库的不足,采用面向对象的数据管理模型,基于平台化的总体思想,设计和实现了一个开放式的基础步态数据支撑平台.提出了包括数据规范、数据采集加工处理、数据管理模式、应用开发和设计等方面的系统解决方案.实践表明系统的数据采集规范、样本数量、步态数据清晰度、基础平台和开放性等指标都超过了同类型的数据库,系统具有较强的扩展性、安全性和可靠性,达到了预期目标.     

Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person's identity with high confidence. It is well recognized that in biometric systems feature extraction and representation are key considerations. Among various feature extraction and representation schemes, coding-based methods are most attractive because they have the merits of high accuracy, robustness, compactness and high matching speed, and thus they have been adopted in many different kinds of biometric systems, such as iris, palmprint, and finger-knuckle-print based ones. However, how to devise a good coding scheme is still an open issue. Recent studies in image processing and applied mathematics have shown that local image features can be well extracted with Riesz transforms in a unified framework. Thus, in this paper we propose to utilize Riesz transforms to encode the local patterns of biometric images. Specifically, two Riesz transforms based coding schemes, namely RCode1 and RCode2, are proposed. They both use 3-bits to represent each code and employ the normalized Hamming distance for matching. RCode1 and RCode2 are thoroughly evaluated and compared with the other 3-bit coding methods on a palmprint database and a finger-knuckle-print database. Experiments show that the proposed methods, especially RCode2, could achieve quite similar verification accuracies with the state-of-the-art method (CompCode) while they need much less time at the feature extraction stage, which renders them better candidates for time critical applications.     

A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences

Parkinson’s Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson’s Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality.     

2D Z-string: A new spatial knowledge representation for image databases

The knowledge structure called the 2D C⁺-string, proposed by Huang et al., to represent symbolic pictures allows a natural way to construct iconic indexes for images. According to the cutting mechanism of the 2D C⁺-string, an object may be partitioned into several subparts. The number of partitioned subparts is bounded to O(n²), where n is the number of objects in the image. Hence, the string length is also bounded to O(n²). In this paper, we propose a new spatial knowledge representation called the 2D Z-string. Since there are no cuttings between objects in the 2D Z-string, the integrity of objects is preserved and the string length is bounded to O(n). Finally, some experiments are conducted to compare the performance of both approaches.     

动态核的形态分解联想算法及抗随机噪声研究

在灰度图像分解算法和动态核形态联想记忆网络的基础上,提出了一种新的联想记忆算法--动态核的形态分解联想算法.该方法显著地提高了联想记忆抗随机噪声的能力,较好地解决了灰度图像在含噪时的联想记忆和识别的问题,从而给出了一种恢复含噪灰度图像的途径,并把该方法推广到了彩色图像的处理.通过实验,验证了该方法的良好性能,取得了理想的结果.     

A new nonconvex approach for image restoration with Gamma noise

In this paper, a new total generalized variational(TGV) model for restoring images with multiplicative noise is proposed, which contains a nonconvex fidelity term and a TGV term. We use a difference of convex functions algorithm (DCA) to deal with the proposed model. For multiplicative noise removal, there exist many models and algorithms, most of which focus on convex approximation so that numerical algorithms with guaranteed convergence can be designed. Unlike these algorithms, we use the DCA algorithm to remove multiplicative noise. By numerical experiments, it is shown that the proposed approach leads to a better solution compared with the gradient projection algorithm for solving the classic multiplicative noise removal models. We prove that the sequence generated by the DCA algorithm converges to a stationary point, which satisfies the first order optimality condition. Finally, we demonstrate the performance of our whole scheme by numerical examples. A comparison with other methods is provided as well. Numerical results demonstrate that the proposed algorithm significantly outperforms some previous methods for multiplicative Gamma noise removal.     

一种影像测量系统中自适应混合噪声滤波方法

介绍了影像测量系统中的噪声来源以及常用的滤波方法。针对均值滤波器和中值滤波器在滤除高斯噪声和脉冲噪声时各自表现出良好的性能,设计了一种针对待测零件图像中混合噪声的自适应滤波方法,系统根据噪声类别灵活选择滤波方法。实验证明改进的滤波策略能获得比传统滤波方法更高的信噪比改善因子。     

A fuzzy filter for the removal of random impulse noise in image sequences

In this paper a new fuzzy filter for the removal of random impulse noise in digital grayscale image sequences is presented. The filter consists of different noise detection and filtering steps, in which the fuzzy set theory is used. This noise detection is based both on spatial and on temporal information and has the aim to prevent the filtering of noise free image pixels. The filtering of the detected noisy pixels is finally performed in a motion compensated way. Experimental results show that our method outperforms other state-of-the-art filters in terms of the peak-signal-to-noise ratio as well as visual quality.     

A robust evolutionary feedforward active noise control system using Wilcoxon norm and particle swarm optimization algorithm

The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown.     

一种用于图像匹配识别的高斯—脉冲混合噪声去除方法

简要分析了无人机所拍摄数字图像中噪声的产生原因与数学分布模型,结合图像匹配的实际需求,提出了一种高斯—脉冲噪声混合噪声去除方法,并采用ASIFT匹配算法对提出去噪方法的实际应用效果进行检验。该方法首先采用自适应中值滤波方法去除图像中的脉冲噪声,然后采用OWT SURE—LET算法去除图像中的高斯白噪声。实验结果表明:该算法不仅运算时间更短,而且图像匹配效果稳定。     

一种基于粗糙集的脉冲噪声图像边缘检测算法

研究并提出了一种图像边缘检测算法,对被脉冲噪声污染的图像进行预处理,从而便于图像的分析、理解等后续操作。与一般算法不同,该算法从图像的边缘特性入手来解决边缘检测问题,以由特性决定的一系列边缘点的约束条件作为算法基础,并用粗糙集理论解决了这些条件的相关问题,进而建立了整个边缘检测算法。计算机仿真表明,本文的算法能够有效地从含噪图像中提取边缘信息,较好地克服了传统算法对噪声的敏感性问题。     

A high speed 1000 fps CMOS image sensor with low noise global shutter pixels

A low read noise 8T global shutter pixel for high speed CMOS image sensor is proposed in this paper.The pixel has a pixel level sample-and-hold circuit and an in-pixel amplifier whose gain is larger than one.Using pixel level sample-and-hold circuit,the KTC noise on FD node can be effectively cancelled by correlated double sampling operation.The in-pixel amplifier with a gain larger than one is employed for reducing the pixel level sample-and-hold capacitors thermal noise and their geometric size.A high speed 1000 fps 256×256 CMOS image sensor based on the pixel is implemented in 0.18μm CMOS process.The chip active area is 5 mm×7 mm with a pixel size of 14μm×14μm.The developed sensor achieves a read noise level as low as 14.8e-while attaining a high fill factor of 40%.The full well capacity can contain 30840e-and the resulting signal dynamic range is 66 dB.     

Toward online education for fingerprint recognition: A proof-of-concept web platform

Recent advances in information and communication technology (ICT) have pushed education systems into the online paradigm. Although fingerprint recognition has received considerable attention from researchers, the concept of creating online or distance education systems for fingerprint recognition systems is a valid problem that requires further investigation. This article addresses the problem and presents a web platform for online education of a fingerprint recognition system. This study focused on the technical design and implementation of the web platform solely as a proof of concept to illustrate the possibility of creating distance education for fingerprint recognition. The developed platform offers students a way to gain knowledge about the full range of fingerprint recognition system technology. The significance of using a web platform stems from the need to supplement students’ theoretical knowledge with practical hands-on experience with information security in general and with fingerprint recognition in particular. Demonstrations such as fingerprint image enhancement and fingerprint feature extraction are included in the current platform prototype. Plots of fingerprint recognition performance curves such as false match rate (FMR) and false non-match rate (FNMR) as functions of the matching threshold curve, the receiver operating characteristic (ROC) curve, and the detection error trade-off (DET) curve are plotted as part of the platform. From a technical viewpoint, the platform was successfully implemented; however, its educational impact needs further evaluation.     

Spontaneous facial expression recognition: A robust metric learning approach

Spontaneous facial expression recognition is significantly more challenging than recognizing posed ones. We focus on two issues that are still under-addressed in this area. First, due to the inherent subtlety, the geometric and appearance features of spontaneous expressions tend to overlap with each other, making it hard for classifiers to find effective separation boundaries. Second, the training set usually contains dubious class labels which can hurt the recognition performance if no countermeasure is taken. In this paper, we propose a spontaneous expression recognition method based on robust metric learning with the aim of alleviating these two problems. In particular, to increase the discrimination of different facial expressions, we learn a new metric space in which spatially close data points have a higher probability of being in the same class. In addition, instead of using the noisy labels directly for metric learning, we define sensitivity and specificity to characterize the annotation reliability of each annotator. Then the distance metric and annotators' reliability is jointly estimated by maximizing the likelihood of the observed class labels. With the introduction of latent variables representing the true class labels, the distance metric and annotators' reliability can be iteratively solved under the Expectation Maximization framework. Comparative experiments show that our method achieves better recognition accuracy on spontaneous expression recognition, and the learned metric can be reliably transferred to recognize posed expressions.     

Context-aware fusion: A case study on fusion of gait and face for human identification in video

Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge.     

CAS-PEAL大规模中国人脸图像数据库及其基本评测介绍

人脸图像数据库是人脸识别算法研究、开发、评测的基础,具有重要的意义．介绍了自行创建并已经部分共享的CAS-PEAL大规模中国人脸图像数据库及其基准测试结果．CAS-PEAL人脸图像数据库中包含了1040名中国人共99450幅头肩部图像．所有图像在专门的采集环境中采集,涵盖了姿态、表情、饰物和光照4种主要变化条件,部分人脸图像具有背景、距离和时间跨度的变化．目前该人脸图像数据库的标准训练、测试子库已经公开发布．与其他已经公开发布的人脸图像数据库相比,CAS-PEAL人脸图像数据库在人数、图像变化条件等方面具有综合优势,将对人脸识别算法的研究、评测产生积极的影响．同时,作为以东方人为主的人脸图像数据库,CAS-PEAL人脸图像数据库也使人脸识别算法在不同人种之间的比较成为可能,利于人脸识别算法在国内的实用化．还给出了两种基准人脸识别算法(Eigenface和Correlation)和两个著名商业系统在该人脸图像数据库上的测试结果,定量分析了该人脸图像数据库对于人脸识别算法的难易程度和人脸识别算法的发展现状．