本文選題：人耳識(shí)別 + 萬有引力場(chǎng)��； 參考：《重慶大學(xué)》2007年碩士論文

【摘要】： 人耳識(shí)別是生物特征識(shí)別技術(shù)研究中的一個(gè)新興領(lǐng)域,它旨在根據(jù)人的外耳特征進(jìn)行身份識(shí)別。人耳識(shí)別可以作為其他生物識(shí)別技術(shù)的有益補(bǔ)充,也可以單獨(dú)應(yīng)用于一些個(gè)體識(shí)別場(chǎng)合。目前人耳識(shí)別技術(shù)在國(guó)內(nèi)外尚處于初步探索研究階段,還沒有形成較為完善的理論體系。作為一種生物特征識(shí)別技術(shù),人耳識(shí)別需要借鑒其他的生物識(shí)別技術(shù)和處理方法,同時(shí)人耳具有其特殊的生理結(jié)構(gòu)和生理位置,必然有其獨(dú)特的處理方法和技術(shù)與之相適應(yīng)。 本文以人耳這一特殊的生物特征為研究對(duì)象,并在前人所做工作的基礎(chǔ)上,以人耳圖像萬有引力場(chǎng)轉(zhuǎn)換、特征提取等關(guān)鍵環(huán)節(jié)為研究重點(diǎn),力圖改善和豐富人耳識(shí)別這種新興的生物特征識(shí)別技術(shù),以期對(duì)后續(xù)研究人員的工作進(jìn)展有所啟發(fā)。 眾所周知,確定特征空間的最終目標(biāo)是在降低原有的維度空間的同時(shí),又能保持正確分類識(shí)別的能力。為了達(dá)到這個(gè)目標(biāo),在人耳生物識(shí)別研究領(lǐng)域中,一種新的萬有引力場(chǎng)轉(zhuǎn)換理論把圖像看作一系列相互吸引的像素粒子,這些粒子就作為萬有引力場(chǎng)源。伴隨力場(chǎng)產(chǎn)生的還有一個(gè)標(biāo)量勢(shì)能場(chǎng),在勢(shì)能場(chǎng)中,人耳輪廓具有光滑的表面,就像很多由山脊連接的山峰一樣。這里的山峰對(duì)應(yīng)的就是勢(shì)能阱,類似地山脊就對(duì)應(yīng)著勢(shì)能通道。本文著重描述了如何利用萬有引力場(chǎng)轉(zhuǎn)換算法來提取力場(chǎng)特征,利用場(chǎng)線方向來定位勢(shì)能通道和勢(shì)能阱,這些勢(shì)能通道和勢(shì)能阱最終將構(gòu)成人耳的特征信息。為了進(jìn)一步說明力場(chǎng)轉(zhuǎn)換具有可逆性、良好的亮度靈敏性及定位迅速等優(yōu)點(diǎn),本文利用自己建立的人耳圖像庫完成了實(shí)驗(yàn)。并通過對(duì)比硬C-均值,模糊C-均值和改進(jìn)模糊C-均值聚類算法的實(shí)驗(yàn)結(jié)果,來說明力場(chǎng)轉(zhuǎn)換理論應(yīng)用于人耳識(shí)別的有效性。本文不僅證明了人耳識(shí)別是一種有著極大發(fā)展?jié)摿Φ纳镒R(shí)別技術(shù),還證明了力場(chǎng)轉(zhuǎn)換具有很好的魯棒性,特別是對(duì)噪聲的容忍度。另外人耳輪廓不必從背景中提取出來,這點(diǎn)也是它的優(yōu)勢(shì)所在。 總的來說,本文主要從力場(chǎng)轉(zhuǎn)換理論的幾個(gè)關(guān)鍵環(huán)節(jié)進(jìn)行了有益的探索與研究,得到了較好的實(shí)驗(yàn)結(jié)果,為人耳識(shí)別技術(shù)的發(fā)展和應(yīng)用提供了有價(jià)值的研究思路。
[Abstract]:Ear recognition is a new research field in biometric recognition. Ear recognition can be used as a useful supplement to other biometric techniques and can also be used in individual recognition situations. At present, the human ear recognition technology is still in the initial stage of exploration and research at home and abroad, and has not yet formed a relatively complete theoretical system. As a biometric recognition technology, human ear recognition needs to draw lessons from other biometric identification techniques and processing methods. At the same time, human ear has its special physiological structure and position, so it must have its unique processing methods and techniques to adapt to it. In this paper, the special biological characteristics of human ear are taken as the research object, and on the basis of the previous work, the key links such as the transformation of gravitational field and feature extraction of human ear image are taken as the focus of the research. This paper tries to improve and enrich the new biometric recognition technology of human ear recognition, so as to enlighten the future work of researchers. It is well known that the ultimate goal of determining feature space is to reduce the original dimension space while maintaining the ability of correct classification and recognition. In order to achieve this goal, in the field of human ear biometrics, a new universal gravitational field transformation theory regards the image as a series of pixel particles that attract each other, and these particles are regarded as the source of gravitational field. The force field is accompanied by a scalar potential energy field in which the human ear has a smooth surface like many peaks connected by ridges. The peak here corresponds to a potential energy trap, similar to a ridge corresponding to a potential energy channel. This paper mainly describes how to extract the force field features by using the universal gravitational field transformation algorithm, and how to locate the potential energy channels and potential energy wells by using the field line direction. These potential energy channels and potential energy wells will eventually form the characteristic information of the human ear. In order to further illustrate the advantages of force field conversion, such as reversibility, good brightness sensitivity and rapid localization, this paper has completed the experiment by using the ear image database established by ourselves. By comparing the experimental results of hard C- mean, fuzzy C- mean and improved fuzzy C- mean clustering algorithm, the validity of applying force field transformation theory to ear recognition is demonstrated. This paper not only proves that ear recognition is a biometric technology with great potential, but also proves that the force field conversion has good robustness, especially the tolerance to noise. In addition, the contour of the ear does not have to be extracted from the background, which is also its advantage. In general, this paper has carried on the beneficial exploration and the research mainly from several key links of the force field conversion theory, has obtained the better experimental result, has provided the valuable research thought for the human ear recognition technology development and the application.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2007
【分類號(hào)】：D918.2

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