指紋U盤(pán)的設(shè)計(jì)

指紋U盤(pán)的設(shè)計(jì),指紋,設(shè)計(jì) Fingerprint Identification By Salil Prabhakar, Anil Jain Fingerprint Matching: Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending. Fingerprint matching techniques can be placed into two categories: minutae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger.? However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. Also this method does not take into account the global pattern of ridges and furrows. The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach.? However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation. Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns. Local ridge structures can not be completely characterized by minutiae. We are trying an alternate representation of fingerprints which will capture more local information and yield a fixed length code for the fingerprint. The matching will then hopefully become a relatively simple task of calculating the Euclidean distance will between the two codes We are developing algorithms which are more robust to noise in fingerprint images and deliver increased accuracy in real-time. A commercial fingerprint-based authentication system requires a very low False Reject Rate (FAR) for a given False Accept Rate (FAR). This is very difficult to achieve with any one technique. We are investigating methods to pool evidence from various matching techniques to increase the overall accuracy of the system. In a real application, the sensor, the acquisition system and the variation in performance of the system over time is very critical. We are also field testing our system on a limited number of users to evaluate the system performance over a period of time. Fingerprint Classification: Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration. An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!). To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database. Fingerprint classification is a technique to assign a fingerprint into one of the several pre-specified types already established in the literature which can provide an indexing mechanism. Fingerprint classification can be viewed as a coarse level matching of the fingerprints. An input fingerprint is first matched at a coarse level to one of the pre-specified types and then, at a finer level, it is compared to the subset of the database containing that type of fingerprints only. We have developed an algorithm to classify fingerprints into five classes, namely, whorl, right loop, left loop, arch, and tented arch. The algorithm separates the number of ridges present in four directions (0 degree, 45 degree, 90 degree, and 135 degree) by filtering the central part of a fingerprint with a bank of Gabor filters. This information is quantized to generate a Finger Code which is used for classification. Our classification is based on a two-stage classifier which uses a K-nearest neighbor classifier in the first stage and a set of neural networks in the second stage. The classifier is tested on 4,000 images in the NIST-4 database. For the five-class problem, classification accuracy of 90% is achieved. For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8%. By incorporating a reject option, the classification accuracy can be increased to 96% for the five-class classification and to 97.8% for the four-class classification when 30.8% of the images are rejected. Fingerprint Image Enhancement A critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. However, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. In order to ensure that the performance of an automatic fingerprint identification verification system will be robust with respect to the quality of the fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We have developed a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and furrow structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithms improves both the goodness index and the verification accuracy. Fingerprint Identifies Arithmetic The fingerprint technique of scans can divided into 2 types generally: identification system, such as AFIS(automatic fingerprint confirm system) and verification system, two kinds of system key of the differentiation is in the fingerprint template. verification the system equally needs to obtain fingerprint image, but this kind of technique doesn't keep complete fingerprint image and it just keeps through some particular datas that some calculate way processings chase a fingerprint in an opposite smaller template(250-1000 word byte).When these datas are pick up after, the fingerprint portrait won't be again keep and can't scan template to rebuild through a finger, either. For this, many companies with domestic and international in the last yearses and its think factory produced many arithmetic in digital ways . Evaluating a excellently arithmetic which commercial and big area expand , not only need from the miscarriage of justice rate of normal regulations, refused to judge a rate, opposite accuracy, refused to ascend a rate etc. parameter to evaluate, a good cal arithmetic includes various, for example: Can enough filter in addition to fingerprint noise? Adapt different angle to press to press? Adapt different fingerprint quality? Whether in consideration of does the high speed match? Can filter a remnants remaining fingerprint information? Whether can order in the as far as possible little characteristic under identify? Adapt a fingerprint dissimilarity the variety of the season? Can handle a too dry or moist fingerprint? Can adapt a dissimilarity to press the pressure degree? Occupy a quite a little memory? Whether very low to the dependence of system? Whether can good movement under various different operation environments? Can conveniently transplant to go to single slice machine system? Can run out the quantity little characteristic to express a fingerprint information? Does the customer feel very comfortable? Whether the development system opened very much to expand a market? Whether through a great deal of test of fingerprint database? Can let the customer experience a transparent test? Have low-down business threshold? Whether have excellent of 1:N performance? Does the software connect whether matches international norm or not? Can adapt the portrait of different quality? Can provide to connect for customer good development? Etc. The principle of fingerprint identifies arithmetic is after the image was picked up which is a high quality and has to be converted into an useful format for it. If image is ash degree, opposite more shallow part will be abandon, but opposite deeper part be become black. The pixel of ridge is been thin by 5-8 to arrive a pixel, so ability the precision position the ridge break point and diverged. Such as: A arithmetic possibility at inspectional image pick and get rid of a detail of the two close detail, because these two details neared too much, because of the scar formation, sweat liquid or dust cause of detail abnormality, the arithmetic is incapable for the dint to these circumstances. Perhaps, one fork to be located on an island form scar perpendicular cut through 2-3 ridges on(may be a false detail) perhaps a ridge.(may be scar formation or dust)All this possible details want to be abandon in this processing. Once a point is indeed settle down, its position be origin(0, 0) of X, Y axile, in the detail the point of the fixed position process, or the place ridge be square upward of the terminal point have a corner.(the circs when the arch break up point appeared will be more complicated) 指紋確認(rèn) Salil Prabhakar, Anil Jain 指紋匹配: 在所有的生物技術(shù)之中，指紋識(shí)別技術(shù)已經(jīng)被成功地應(yīng)用于很多場(chǎng)合。每個(gè)人都知道指紋具有唯一不可變的特性。一個(gè)人的指紋是由手指的表面上一系列脊和溝做成的。指紋的獨(dú)特性是由脊和溝的式樣和細(xì)節(jié)點(diǎn)決定，細(xì)節(jié)點(diǎn)是發(fā)生一個(gè)脊分叉或一個(gè)脊終止時(shí)的當(dāng)?shù)氐奶卣鼽c(diǎn)。 指紋匹配技術(shù)可以分為兩種: 基于細(xì)節(jié)的和基于相互關(guān)系的。以細(xì)節(jié)為基礎(chǔ)的技術(shù)首先發(fā)現(xiàn)細(xì)節(jié)點(diǎn)，然后在手指上映射出他們的相對(duì)位置。然而，當(dāng)使用這種方法時(shí)還有一些困難，當(dāng)指紋質(zhì)量低的時(shí)候，正確地吸取細(xì)節(jié)點(diǎn)很困難。這一方法也不考慮全球的指紋的脊和溝的式樣。以相互關(guān)系為基礎(chǔ)的方法能夠克服以細(xì)節(jié)為基礎(chǔ)方法的一些困難。然而，它也有一些自己的缺點(diǎn)。以相互關(guān)系為基礎(chǔ)的技術(shù)需要精確定位登記點(diǎn)而且被圖像翻譯和旋轉(zhuǎn)所影響。 基于細(xì)節(jié)的指紋匹配在匹配不同尺寸(未注冊(cè)的) 的細(xì)節(jié)式樣的指紋方面有問(wèn)題，當(dāng)?shù)氐募菇Y(jié)構(gòu)不能完全地?fù)碛屑?xì)節(jié)的特點(diǎn)。 我們正在嘗試為指紋取得更多的當(dāng)?shù)財(cái)?shù)據(jù)并且產(chǎn)生一個(gè)固定的長(zhǎng)度密碼。這時(shí)匹配將會(huì)變成像希望的那樣計(jì)算在這二個(gè)密碼之間的歐幾里得幾何距離的相對(duì)簡(jiǎn)單工作。 我們正在發(fā)展更強(qiáng)健的在指紋圖像中去除噪音性強(qiáng)，遞送準(zhǔn)確性強(qiáng)，實(shí)時(shí)性強(qiáng)的運(yùn)算法則。一個(gè)商業(yè)的指紋確認(rèn)系統(tǒng)需要非常低的錯(cuò)誤率 (FAR) 一個(gè)可以接受的錯(cuò)誤比率.(FAR)這對(duì)于任何的一項(xiàng)技術(shù)都是一個(gè)難點(diǎn)。我們正在研究各種不同的相配技術(shù)來(lái)增加系統(tǒng)的準(zhǔn)確性。在一個(gè)實(shí)時(shí)的應(yīng)用環(huán)境中或在傳感器中，系統(tǒng)的執(zhí)行時(shí)間變的越來(lái)越重要。我們也是把我們的系統(tǒng)實(shí)地試驗(yàn)在少數(shù)用戶(hù)上一段時(shí)間來(lái)評(píng)估我們的系統(tǒng)。 指紋分類(lèi): 每天大量的指紋被收集存儲(chǔ)應(yīng)用在不同的場(chǎng)合，例如法醫(yī)，通路控制和駕駛員執(zhí)照登記。以指紋為基礎(chǔ)的自動(dòng)識(shí)別系統(tǒng)需要輸入一個(gè)與數(shù)據(jù)庫(kù)中相匹配的指紋 (聯(lián)邦調(diào)查局?jǐn)?shù)據(jù)庫(kù)大約包含七千萬(wàn)個(gè)指紋!)。為了減少搜尋時(shí)間和計(jì)算的復(fù)雜性，需要對(duì)這些指紋按正確和一致的方式分類(lèi)，以便輸入指紋時(shí)只需要在數(shù)據(jù)庫(kù)中找到其中匹配的一個(gè)子集而已。 指紋分類(lèi)是一種可以預(yù)先分配一個(gè)指紋進(jìn)入已經(jīng)分類(lèi)好的幾個(gè)類(lèi)型之中并提供一種分度裝置的技術(shù)。指紋分類(lèi)可以看作是大概的粗糙的指紋類(lèi)型的相配。 一個(gè)輸入指紋首先對(duì)預(yù)先存儲(chǔ)的類(lèi)型大致相配，然后,在詳細(xì)的分析，把他和只包含那一個(gè)類(lèi)型指紋的數(shù)據(jù)庫(kù)的子集相比較。我們正在研究一種運(yùn)算法則——把指紋分類(lèi)為五個(gè)類(lèi)型，即螺旋狀紋、右回旋，左回旋，拱形，帳篷形。運(yùn)算法則被一個(gè)Gabor過(guò)濾器分開(kāi)過(guò)濾為四個(gè)方向(0 度、 45 度、 90 度和 135 度)。這些數(shù)據(jù)被量化產(chǎn)生作為分類(lèi)的指紋編碼。我們的分類(lèi)以一個(gè)二階段的分類(lèi)器為基礎(chǔ)。第一個(gè)階段用一個(gè)K最近的分類(lèi)器和第二個(gè)階段用一組類(lèi)神經(jīng)網(wǎng)路分類(lèi)器。在分類(lèi)器NIST-4 數(shù)據(jù)庫(kù)中測(cè)試了4,000個(gè)圖像。 對(duì)于五種類(lèi)型的問(wèn)題，分類(lèi)的準(zhǔn)確性達(dá)到90%。對(duì)于四種類(lèi)型的問(wèn)題 (拱門(mén)和帳篷型組合在一個(gè)類(lèi)型之內(nèi))，我們能夠達(dá)成94.8%的分類(lèi)準(zhǔn)確性。當(dāng)合并不合格者選項(xiàng)時(shí)，分類(lèi)準(zhǔn)確性能達(dá)到為五種分類(lèi)時(shí)增加到 96% 和至 97.8% ，為四種類(lèi)型時(shí) 30.8% 的被拒絕。 指紋圖像增強(qiáng)： 在自動(dòng)指紋識(shí)別系統(tǒng)中一個(gè)重要的步驟是從輸入的指紋圖像中自動(dòng)地而且可靠地吸取指紋細(xì)節(jié)。然而，這種獲取指紋細(xì)節(jié)的計(jì)算法則很?chē)?yán)重地依賴(lài)輸入指紋圖像的質(zhì)量。為了確定自動(dòng)指紋識(shí)別的效果，確認(rèn)系統(tǒng)是對(duì)指紋質(zhì)量的重要衡量系統(tǒng),有必要將提高指紋運(yùn)算法則的質(zhì)量作為細(xì)節(jié)提取模塊的一部分。我們已經(jīng)設(shè)計(jì)出了一個(gè)快速識(shí)別指紋的運(yùn)算法則, 能盡量地增強(qiáng)脊的清晰度，還能以輸入指紋圖像的溝結(jié)構(gòu)為基礎(chǔ)，估計(jì)當(dāng)?shù)氐募苟ǚ轿缓皖l率。我們已經(jīng)使用被吸取的細(xì)節(jié)點(diǎn)的索引和在線(xiàn)指紋確認(rèn)系統(tǒng)來(lái)評(píng)估圖像運(yùn)算法則的表現(xiàn)。實(shí)驗(yàn)的結(jié)果表示這種運(yùn)算法則能提高和改善確認(rèn)的準(zhǔn)確性。 指紋識(shí)別技術(shù)的算法： 指紋掃描技術(shù)大體可分為兩類(lèi)：確認(rèn)(identification)系統(tǒng)，如 AFIS（自動(dòng)指紋確認(rèn)系統(tǒng)）和核對(duì)(verification)系統(tǒng)，兩種系統(tǒng)主要區(qū)別在指紋模板。核對(duì) (verification) 系統(tǒng)同樣需要獲取指紋圖象，但這種技術(shù)并不保存完整的指紋圖象，它只是通過(guò)一些算法處理把指紋的一些特定的數(shù)據(jù)保存在一個(gè)相對(duì)較小的模板（ 250-1000 字節(jié)）中。當(dāng)這些數(shù)據(jù)被拾取后，指紋圖象將不再被保存，也不能通過(guò)手指掃描模板來(lái)重建。為此，多年來(lái)國(guó)內(nèi)外公司及其研究機(jī)構(gòu)產(chǎn)生了許多數(shù)字化的算法。 評(píng)估一個(gè)優(yōu)秀的能夠商業(yè)化大面積推廣的指紋算法，不僅要從常規(guī)的誤判率，拒判率，相對(duì)準(zhǔn)確率，拒登率等參數(shù)來(lái)評(píng)估，一個(gè)好的算法包括有許多方面，例如：能否夠?yàn)V除指紋噪音？是否適應(yīng)不同的角度去按壓？是否適應(yīng)不同的指紋質(zhì)量？是否考慮到高速匹配？是否能夠過(guò)濾殘余指紋信息？是否能夠在盡量少的特征點(diǎn)下識(shí)別？是否適應(yīng)指紋不同季節(jié)的變化？是否能夠處理過(guò)于干燥或濕潤(rùn)的指紋 ? 是否能夠適應(yīng)不同按壓力度？是否占有非常少的內(nèi)存？是否對(duì)系統(tǒng)的依賴(lài)性很低？是否能夠在各種不同的操作環(huán)境下良好的運(yùn)行 ? 是否可以方便的移植到單片機(jī)系統(tǒng)中去？是否可以用盡量少的特征表述指紋信息？客戶(hù)是否感到很舒適？是否有非常開(kāi)放的開(kāi)發(fā)體系來(lái)推廣市場(chǎng)？是否經(jīng)過(guò)大量的指紋庫(kù)的測(cè)試？是否能夠讓客戶(hù)經(jīng)歷透明的測(cè)試 ? 是否具備非常低的商業(yè)門(mén)檻 ? 是否有優(yōu)秀的 1:N 的表現(xiàn)？軟件接口是否符合國(guó)際規(guī)范？是否能適應(yīng)不同質(zhì)量的圖象？是否能夠提供給用戶(hù)良好的開(kāi)發(fā)接口？等等 指紋識(shí)別技術(shù)算法的工作原理為當(dāng)一個(gè)高質(zhì)量的圖象被拾取后，它必須被轉(zhuǎn)換成一個(gè)有用的格式。如果圖象是灰度圖象，相對(duì)較淺的部分會(huì)被舍棄，而相對(duì)較深的部分被變成了黑色。脊的象素由 5 到 8 個(gè)被縮細(xì)到一個(gè)象素，這樣就能精確定位脊斷點(diǎn)和分岔了。如：一個(gè)算法可能在檢索圖象時(shí)剔除兩個(gè)鄰近細(xì)節(jié)中的一個(gè)細(xì)節(jié)，因?yàn)檫@兩個(gè)細(xì)節(jié)太接近了，由于疤痕，汗液或灰塵導(dǎo)致的細(xì)節(jié)異常，算法對(duì)于這些情況是無(wú)能為力的?；蛘?，一個(gè)分岔位于一個(gè)島形痕之上（可能是錯(cuò)誤細(xì)節(jié)）或者一個(gè)脊垂直穿過(guò)兩到三個(gè)脊（可能是疤痕或灰塵）。所有這些可能的細(xì)節(jié)都要在這個(gè)處理過(guò)程中被舍棄。一旦一個(gè)點(diǎn)被確定下來(lái)，它的位置就是 X，Y 軸和原點(diǎn)（0 ，0），在細(xì)節(jié)點(diǎn)的定位過(guò)程中，與所在脊方向上的終點(diǎn)有一個(gè)夾角（拱形斷點(diǎn)的情況法則將更復(fù)雜）。