【摘要】：近年來,生物特征識別技術(shù)得到了飛速發(fā)展,然而傳統(tǒng)的單一模態(tài)的身份識別技術(shù)存在著一定的局限性,導(dǎo)致了該技術(shù)在實際應(yīng)用中會出現(xiàn)一些不必要的麻煩。伴隨數(shù)據(jù)融合技術(shù)的日漸成熟,多模態(tài)生物特征識別這一利用多種生物特征進(jìn)行數(shù)據(jù)融合識別的身份識別技術(shù)獲得了很大的技術(shù)支持,也促使該技術(shù)能夠更快的進(jìn)入我們?nèi)粘Ｉ钪小１疚耐ㄟ^對常見的單模態(tài)生物特征、傳統(tǒng)的多模態(tài)生物特征的融合策略等方面的研究,最終采用指紋和虹膜兩種單模態(tài)生物特征,在多模態(tài)生物特征識別中的特征層這一層次進(jìn)行了融合識別的實驗。主要工作總結(jié)如下:1.深入了解并研究多模態(tài)生物特征識別在各個層次進(jìn)行數(shù)據(jù)融合的相關(guān)方法,包括串并聯(lián)和基于典型相關(guān)分析(CCA)等在特征層融合、基于最小二乘法和Fisher判別等在分?jǐn)?shù)層融合、加權(quán)法和多數(shù)投票法等在決策層融合。2.在深入研究多模態(tài)生物特征融合識別的基礎(chǔ)上,提出基于指紋和虹膜的特征層融合模型,與現(xiàn)有的特征層融合的策略進(jìn)行了對比和分析,通過實驗論證了多模態(tài)融合識別的識別率相比較單模態(tài)識別更高一些這一論點,并驗證了基于典型相關(guān)分析的融合算法在多模態(tài)生物特征識別中的有效性。3.針對基于典型相關(guān)分析的融合算法的不足,提出一種基于矩陣變換的判別典型相關(guān)性分析(MDCCA)的多模態(tài)生物特征識別算法,在同一環(huán)境下對兩種算法進(jìn)行了實驗,實驗驗證了該算法的有效性。4.在本文提出的新算法的基礎(chǔ)上,完成了比較完整的多模態(tài)生物特征識別過程。本文研究的算法內(nèi)容在數(shù)據(jù)融合和多模態(tài)生物特征識別算法的領(lǐng)域都有著一定的參考價值。
[Abstract]:In recent years, biometric identification technology has been developed rapidly. However, the traditional single mode identification technology has some limitations, which leads to some unnecessary problems in practical application. With the maturation of data fusion technology, multi-modal biometric recognition, which uses a variety of biometric features for data fusion recognition, has received a lot of technical support. It also enables the technology to enter our daily lives more quickly. In this paper, the common single-mode biometrics and the traditional multi-modal biometric fusion strategies are studied. Finally, two kinds of single-mode biometrics, fingerprint and iris, are adopted in this paper. Experiments of fusion recognition are carried out at the level of feature layer in multimodal biometric recognition. The main work is summarized as follows: 1. The related methods of multimodal biometric recognition data fusion at various levels are deeply understood and studied, including series-parallel fusion and (CCA) fusion based on canonical correlation analysis, fractional fusion based on least square method and Fisher discriminant, etc. The weighted method and the majority voting method are merged at the decision-making level. 2. On the basis of in-depth research on multi-modal biometric fusion, a feature layer fusion model based on fingerprint and iris is proposed, which is compared with the existing feature layer fusion strategy. The conclusion that the recognition rate of multimodal fusion recognition is higher than that of single mode recognition is proved by experiments, and the validity of fusion algorithm based on canonical correlation analysis in multi-modal biometric recognition is verified. Aiming at the shortcomings of the fusion algorithm based on canonical correlation analysis, a multi-modal biometric recognition algorithm based on matrix transform for discriminating canonical correlation analysis (MDCCA) is proposed, and the two algorithms are tested in the same environment. Experimental results show that the algorithm is effective. 4. 4. On the basis of the new algorithm proposed in this paper, a complete multimodal biometric recognition process is completed. The algorithm studied in this paper has some reference value in the field of data fusion and multi-modal biometric recognition.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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