【導(dǎo)讀】計(jì)算機(jī)科學(xué)與技術(shù)畢業(yè)論文--人臉識(shí)別技術(shù)綜述。隨著社會(huì)信息化網(wǎng)絡(luò)化得不斷發(fā)展個(gè)人身份趨于數(shù)字化隱性化如何準(zhǔn)確的。鑒定確保信息安全得到越來(lái)越多的重視人臉識(shí)別一種應(yīng)用比較廣泛的生物識(shí)別。方法在基于人臉固有的生物特征信息利用模式識(shí)別和圖行圖像處理技術(shù)來(lái)對(duì)個(gè)。人身份進(jìn)行鑒定在國(guó)家安全計(jì)算機(jī)交互家庭娛樂(lè)等其他很多領(lǐng)域發(fā)揮著舉足輕。重的作用能提高辦事效率防止社會(huì)犯罪等有著重大的經(jīng)濟(jì)和社會(huì)意義。本文主要研究了人臉識(shí)別在圖像檢測(cè)識(shí)別方面的一些常用的方法由于。圖像處理的好壞直接影響著定位和識(shí)別的準(zhǔn)確率因此本文對(duì)圖像的一些識(shí)別算。法做了著重的介紹例如基于二維Gabor小波矩陣表征人臉的識(shí)別算法基于模型。匹配人臉識(shí)別算法等此外本文還提及了一般人臉識(shí)別系統(tǒng)的設(shè)計(jì)并著重介紹了。圖像預(yù)處理環(huán)節(jié)的光線(xiàn)補(bǔ)償圖像灰度化等技術(shù)使圖像預(yù)處理模塊在圖像處理過(guò)。13國(guó)內(nèi)外現(xiàn)狀與趨勢(shì)7. 國(guó)內(nèi)的發(fā)展概況8. 基于分塊小波變換與奇異值閾值壓縮的人臉特征提取與識(shí)別算法17

　　

【正文】 hreshold is then applied to make the final verification decision d q g d threshold accept d threshold reject Equ 41 Verification Tests The primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a persons most likely identity from a set of potential matches in a database In order to assess a given systems ability to perform these tasks a variety of evaluation methodologies have arisen Some of these analysis methods simulate a specific mode of operation ie secure site access or surveillance while others provide a more mathematical description of data distribution in some classification space In addition the results generated from each analysis method may be presented in a variety of formats Throughout the experimentations in this thesis we primarily use the verification test as our method of analysis and parison although we also use Fishers Linear Discriminant to analyse individual subspace ponents in section 7 and the identification test for the final evaluations described in section 8 The verification test measures a systems ability to correctly accept or reject the proposed identity of an individual At a functional level this reduces to two images being presented for parison for which the system must return either an acceptance the two images are of the same person or rejection the two images are of different people The test is designed to simulate the application area of secure site access In this scenario a subject will present some form of identification at a point of entry perhaps as a swipe card proximity chip or PIN number This number is then used to retrieve a stored image from a database of known subjects often referred to as the target or gallery image and pared with a live image captured at the point of entry the query image Access is then granted depending on the acceptancerejection decision The results of the test are calculated according to how many times the acceptreject decision is made correctly In order to execute this test we must first define our test set of face images Although the number of images in the test set does not affect the results produced as the error rates are specified as percentages of image parisons it is important to ensure that the test set is sufficiently large such that statistical anomalies bee insignificant for example a couple of badly aligned images matching well Also the type of images high variation in lighting partial occlusions etc will significantly alter the results of the test Therefore in order to pare multiple face recognition systems they must be applied to the same test set However it should also be noted that if the results are to be representative of system performance in a real world situation then the test data should be captured under precisely the same circumstances as in the application environmentOn the other hand if the purpose of the experimentation is to evaluate and improve a method of face recognition which may be applied to a range of application environments then the test data should present the range of difficulties that are to be overe This may mean including a greater percentage of difficult images than would be expected in the perceived operating conditions and hence higher error rates in the results produced Below we provide the algorithm for executing the verification test The algorithm is applied to a single test set of face images using a single function call to the face recognition algorithm CompareFaces FaceA FaceB This call is used to pare two facial images returning a distance score indicating how dissimilar the two face images are the lower the score the more similar the two face images Ideally images of the same face should produce low scores while images of different faces should produce high scores Every image is pared with every other image no image is pared with itself and no pair is pared more than once we assume that the relationship is symmetrical Once two images have been pared producing a similarity score the groundtruth is used to determine if the images are of the same person or different people In practical tests this information is often encapsulated as part of the image filename by means of a unique person identifier Scores are then stored in one of two lists a list containing scores produced by paring images of different people and a list containing scores produced by paring images of the same person The final acceptancerejection decision is made by application of a threshold Any incorrect decision is recorded as either a false acceptance or false rejection The false rejection rate FRR is calculated as the percentage of scores from the same people that were classified as rejections The false acceptance rate FAR is calculated as the percentage of scores from different people that were classified as acceptances For IndexA 0 to length TestSet For IndexB IndexA1 to length TestSet Score CompareFaces TestSet[IndexA] TestSet[IndexB] If IndexA and IndexB are the same person Append Score to AcceptScoresList Else Append Score to RejectScoresList For Threshold Minimum Score to imum Score FalseAcceptCount FalseRejectCount 0 For each Score in RejectScoresList If Score Threshold Increase FalseAcceptCount For each Score in AcceptScoresList If Score Threshold Increase FalseRejectCount FalseAcceptRate FalseAcceptCount Length AcceptScoresList FalseRejectRate FalseRejectCount length RejectScoresList Add plot to error curve at FalseRejectRate FalseAcceptRate These two error ra