人脸识别技术及其应用

人脸识别技术是一种新型的进行身份鉴定的识别技术，广泛应用与各个领域。本文针对人脸识别技术及其应用进行了简的分析和阐述。     

人脸识别技术在应用中的局限

随着发展,人脸识别技术已慢慢走近人们现实生活。最近,网易称将在其免费邮箱登录安全服务上采用人脸识别。这让不少网友好奇,人脸识别能起到哪些作用？实用性又如何？然而,人脸识别技术并非传奇,它也有自身的局限性,还无法达到人们预期的识别率100％。     

基于网络的智慧校园技术分析

结合浙江温州肯恩大学的智慧校园的建设思路,重点从建筑设备管理系统、公共信息系统、综合能源管理系统、计算机网络系统、有线电视系统、多媒体会议系统、一卡通系统等11个方面分析了网络智慧校园的技术支撑,最后结合浙江温州肯恩大学的智慧校园的建设现状对智慧校园的应用进行了总结。     

浅谈智慧校园建设

随着云计算、物联网、移动互联网等新一代信息技术的发展,教育信息化建设逐渐从"数字校园"过渡到"智慧校园",本文根据近期在北京市海淀区、朝阳区几所中小学的智慧校园的设计和实施情况总结概括了智慧校园建设的总体目标和分期目标以及一期基础工程应重点实施的内容,希望为新技术、新理念的落地实施提供一些帮助。     

大数据时代智慧校园的信息可视化设计应用研究

目的探究大数据时代信息可视化设计在智慧校园中的应用。方法基于大数据时代,由智慧地球这一概念引伸出了智慧校园的概念,介绍了智慧校园的发展背景及目前国内外智慧校园的发展现状,总结了智慧校园的5个特点,并通过挖掘智慧校园与信息可视化设计之间的联系,分析研究了具体的信息可视化设计应用实例。结论论述了适用于智慧校园的信息可视化系统和交互端的设计框架,提出了信息可视化设计在智慧校园建设中的应用展望及对策。     

区块链技术在智慧能源体系中的应用研究

区块链是目前打通物理实体、虚拟数据直至价值层面最合适的技术手段之一,其核心是依靠分布式共识机制实现价值的可靠重构和转移。区块链的技术原理和价值特性与智慧能源的内在诉求不谋而合,匹配智慧能源的多主体应用场景。基于区块链的技术特性,对其在智慧能源体系中的适用性进行分析,提出具体的技术选型和构架设计方案,促进能源区块链技术在智慧能源、能源互联网领域的发展。     

浅谈服务器集群技术在数字化校园中的应用

对服务器集群技术做简单介绍,同时对服务器集群技术在数字化校园中的应用进行探讨。     

人脸识别技术的研究

人脸识别技术是模式识别领域一个重要的研究课题之一,它具有唯一性与可靠性等且应用广泛。文中概括介绍了人脸识别技术的应用前景和人脸识别的i警墅j哆人脸检测和特征提取的方法进行了简单比较。     

二维码技术在校园导航系统设计中的应用

手机二维码不但可以印刷在报纸、杂志、广告、图书、包装以及个人名片上,用户还可以通过手机扫描二维码,或输入二维码下面的号码即可实现快速手机上网功能,并随时随地下载图文、了解相关信息。文章基于二维码技术实现郧阳师专文化校园景观自助导游系统,游客通过手机快拍布置于文化景点的二维码标牌,就可以获得关于该处景点的视频、图片、讲解等,游客可以一边游玩,一边听讲解,实现游客自助。最后在分析二维码的技术特点后,提出在建立基于二维码的校园导航系统过程中可能存在的问题,以及一些解决办法。实现"智慧旅游"相关技术在郧阳师专文化校园景观旅游上的具体应用,从而更好的传播郧阳师专校园文化的底蕴。     

大数据及物联网技术在智慧城市中的应用

近些年来,随着我国社会经济的发展,科学技术的进步,我国的城市化建设水平也在不断地提高,在这种形势下,"大数据以及物联网技术"就成了智慧城市建设过程中的重点。但是由于数据与物联网技术之间没有实现良好的结合,因此就无法进一步推动智慧化城市的建设。要想有效地解决上述问题,首先就要提高大数据及物联网技术在智慧城市建设过程中的应用水平,进一步提高我国城市建设的智慧化水平。     

China's first voiceprint recognition technology standardization research base

正The inauguration ceremony for the first standardization research base of voiceprint recognition technology and natural language processing technology application recently took place in Xiamen,marking the establishment of China’s first national artificial intelligence technology and application standardization research base.According to Xian Kuitong,Director-General of Sub-Institute of High and New TechnologyInformation,CNIS in his opening address,the development and implementation of standards for     

Three-dimensional sensor-based face recognition

We describe a face recognition system based on two different three-dimensional (3D) sensors. We use 3D sensors to overcome the pose-variation problems that cannot be effectively solved in two-dimensional images. We acquire input data based on a structured-light system and compare it with 3D faces that are obtained from a 3D laser scanner. Owing to differences in structure between the input data and the 3D faces, we can generate the range images of the probe and stored images. For estimating the head pose of input data, we propose a novel error-compensated singular-value decomposition that geometrically estimates the rotation angle. Face recognition rates obtained with principal component analysis on various range images of 35 people in different poses show promising results.     

An overview of smart technology

In recent years there has been a seemingly ever-increasing use of the synonymous adjectives ‘smart’ and ‘intelligent’ to describe a diverse range of materials, structures, systems and technologies.^1-4 The origin of this terminology can be dated to the early 1980s when researchers working mainly in the US, and funded predominantly from defence budgets, began to examine the potential of combining advanced materials and sensors with powerful and compact computers to produce futuristic systems able to monitor their operating environment in real time and respond appropriately. Public awareness of this technology was given an enormous boost by the prominence given to use of ‘smart’ munitions during the Gulf war. Various articles appearing in popular science journals^5–7 and broadsheet newspapers in the months following the successful conclusion of this conflict served to maintain interest and it became fashionable to examine the use of ‘smart’ technology in industrial applications far removed from those originally envisaged in aerospace and defence. Various dedicated university research groups were formed at around this time (among the first in the UK being the Smart Structures Research Institute at Strathclyde University), often bringing together academics who had been working on ‘smart’ technologies for several years without realising it! Over the last five years researchers working in most of the major industrial sectors have given at least some thought to how they might apply ‘smart’ technology, important areas being in transport, building, civil infrastructure, biomedicine, sport and leisure, power generation and oil, gas and petrochemical. Packaging has not been left out of this process, with the prospects for ‘intelligent packaging’ being assessed most notably by CEST and Pira International in a report prepared in 1992 under DTI funding.⁸ Interest in ‘smart’ packaging has been sustained over the four years since this pioneering study, with the focus of attention gradually shifting from abstract conjecture to practical application. Notwithstanding the attention now being devoted to all things ‘smart’, the underlying concepts are still only poorly understood in many quarters and the word must warrant some sort of prize for the proportion of times that it is inaccurately applied. This paper presents a systematic definition of ‘smart’ technology and goes on to review very briefly some of the major advances being made under this technological umbrella. The UK's Defence Research Agency (DRA), like its American counterparts, has been active in ‘smart’ technology from its earliest days and has, for obvious reasons, concentrated mainly on aerospace and defence applications. However, with the launch in April 1994 of the DRA's Structural Materials Centre (SMC), committed explicitly to promoting wealth creation via the dual (i.e. military and civil) use of technology, there has been a conscious effort to identify wider opportunities for the exploitation of the contributing ‘smart’ technologies. Some ideas relevant to packaging which are currently being developed in conjunction with the DRA's Packaging Authority are outlined, together with an invitation for interested companies to participate in various DRA-led joint development programmes. © 1997 John Wiley & Sons, Ltd.     

Probabilistic approach to face recognition

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore, decoupling of the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. The aim of this article is to present a robust face recognition technique based on the extraction and matching of probabilistic graphs drawn on scale invariant feature transform (SIFT) features related to independent face areas. The face matching strategy is based on matching individual salient facial graphs characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster–Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the Olivetti Research Lab (ORL) and the Indian Institute of Technology Kanpur (IITK) face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique, even in the case of partially occluded faces.     

关于智慧家居技术的研究

针对目前国内大部分智能家居功能的局限以及实用性上的不足;如视频的远程传输,系统的远程实时控制等问题;基于人们对智慧家居系统的实际的需求,设计了一种智慧家居系统,该系统结合了以太网通信技术、WIFI网络技术、ZIGBEE通信技术等先进的技术手段,实现了数据的远程传输与控制,同时具有较大的灵活性;可以根据实际情况进行相应的裁剪和扩充。     

监督式正交迹比判别投影在图像集人脸识别中的应用

研究、分析了人脸识别中提取原始数据特征的已有方法,在此基础上给出了一种应用监督式正交迹比判别投影(SOTRDP)的新型特征提取方法,即SOTRDP方法。不同于现有的非监督判别投影(UDP)方法,SOTRDP方法能够同时利用局部信息和类别信息建立相似性矩阵。在利用改进局部切空间对齐(ILTSA)非线性降维的基础上,利用聚类中心或最靠近它的样本作为输入,拓展SOTRDP用于图像集人脸识别。在PIE 和Honda/UCSD人脸数据库上的实验结果验证了所提方法的有效性。     

绿色建筑技术在高等学校建筑的应用分析

高等学校校园建筑不同于一般的工业和民用建筑,是一个有机联系的整体,具有显著园区能耗的特点,绿色建筑技术在高等学校中有很好的利用前景.本文通过对3所高校建筑用能的分析,指出在照明、绿化和景观用水、洗浴用热水中采用绿色建筑技术,可有效的节约能源消耗.为高校开展节能减排、建设绿色校园提供参考.     

多彩色空间相关分析的人脸识别算法

人脸识别是当前人工智能和模式识别的研究热点,得到了广泛的关注.基于对不同色彩空间数据的分析,论文提出了多彩色空间典型相关分析的人脸识别方法.文中对2维的Contourlet变换特性进行了分析和讨论,利用Contourlet的多尺度,方向性和各向异性等特点,提出了一种基于Contourlet变换的彩色人脸识别算法.算法对原图进行Contourlet分解,对分解得到的低频和高频图像进行cca分析.典型相关分析是一种有效的分析方法,其实际应用十分广泛.低频系数反映图像的轮廓信息,高频系数反映图像的细节信息,使用cca充分利用不同频率的信息,使不同色彩空间的不同分辨率图形的相关性达到最大,得到投影系数,最后,采用决策级最近邻分类器完成人脸识别.在对彩色人脸数据库AR的识别实验中,该算法识别率达到98％以上,与传统算法相比,该算法不仅既有良好的识别结果,而且具有很快的运算速度.     

Improving representation-based classification for robust face recognition

The sparse representation classification (SRC) method proposed by Wright et al. is considered as the breakthrough of face recognition because of its good performance. Nevertheless it still cannot perfectly address the face recognition problem. The main reason for this is that variation of poses, facial expressions, and illuminations of the facial image can be rather severe and the number of available facial images are fewer than the dimensions of the facial image, so a certain linear combination of all the training samples is not able to fully represent the test sample. In this study, we proposed a novel framework to improve the representation-based classification (RBC). The framework first ran the sparse representation algorithm and determined the unavoidable deviation between the test sample and optimal linear combination of all the training samples in order to represent it. It then exploited the deviation and all the training samples to resolve the linear combination coefficients. Finally, the classification rule, the training samples, and the renewed linear combination coefficients were used to classify the test sample. Generally, the proposed framework can work for most RBC methods. From the viewpoint of regression analysis, the proposed framework has a solid theoretical soundness. Because it can, to an extent, identify the bias effect of the RBC method, it enables RBC to obtain more robust face recognition results. The experimental results on a variety of face databases demonstrated that the proposed framework can improve the collaborative representation classification, SRC, and improve the nearest neighbor classifier.     

Analysis of unsupervised learning techniques for face recognition

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010