本文選題：手指心電信號 + 生物識別　； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：在高度信息化的現(xiàn)代社會中,隨著交通、網(wǎng)絡(luò)和通訊的飛速發(fā)展,人類的活動范圍越來越大,在實現(xiàn)個體的身份識別的時候,人們面臨的安全問題也越來越嚴(yán)重。生物特征識別技術(shù)(Biometrics)是運用個體一些獨有的并且長期穩(wěn)定的生物特征進行身份辨識的一種技術(shù),例如虹膜、指紋、臉象和聲音、筆跡、步態(tài)等,具有很高的準(zhǔn)確度與可靠度,但也同樣面臨著安全問題,如假冒指紋、虛假虹膜、筆跡的模仿等,所以具備高防偽性的新穎生物特征識別技術(shù)迫切需要被提出。心電信號(Electrocardiogram,ECG)包含著人體獨一無二的身份信息,突出特點是具有很高的防偽能力。而且近年隨著小體積、低能耗、無需導(dǎo)電膠和易集成的心電采集芯片的出現(xiàn),實現(xiàn)了在手指端實現(xiàn)心電的采集,可以普遍的應(yīng)用到家庭生活當(dāng)中,更方便快捷的實現(xiàn)個體身份識別。本文提出了基于手指心電信號分析的個體身份辨識算法的研究。首先,對手指心電信號用小波軟閾值和遺傳算法相結(jié)合的方法進行去噪;其次,研究了手指心電信號的稀疏特性,提出了基于KSVD+PCA下稀疏編碼的手指心電信號身份識別算法和基于改進的標(biāo)簽一致LC-KSVD的手指心電身份識別算法,最后并采用兩個手指心電數(shù)據(jù)庫驗證了這兩個算法,取得了較大的識別率。本文的工作主要為:1、闡述了心電信號的產(chǎn)生機理、波形特點及ECG采集方式和基于手指心電的身份識別算法的發(fā)展,介紹了ECG的識別評價指標(biāo)和兩個手指心電數(shù)據(jù)庫,為手指心電信號應(yīng)用于個體身份識別技術(shù)領(lǐng)域提供了理論基礎(chǔ)。2、提出基于K-SVD+PCA下稀疏編碼的手指心電身份識別算法。首先對手指ECG用小波軟閾值和遺傳算法相結(jié)合的方法去噪,經(jīng)過R峰檢測、單周期劃分、歸一化,得到P-QRS-T單周期波群,結(jié)合手指心電特性,提取P-QRS-T波群構(gòu)成特征向量并構(gòu)建字典模板模型,用KSVD+PCA訓(xùn)練成冗余字典,然后對每一部分特征向量進行稀疏編碼,實現(xiàn)在該字典上的稀疏表示。最后利用兩個心電數(shù)據(jù)庫(CYBHi,Surface ECG data)測試了算法性能,取得98.333%和100%的識別率。3、提出基于改進的標(biāo)簽一致LC-KSVD的手指心電身份識別算法。首先提取手指心電信號的平均單周期的P-QRS-T波群作為訓(xùn)練樣本,然后提出自適應(yīng)子字典和可調(diào)類標(biāo)簽對標(biāo)簽一致性的LC-KSVD(Label consistent KSVD,LC-KSVD)進行改進,然后利用改進的LC-KSVD1和LC-KSVD2算法完成識別。當(dāng)輸入信號和字典原子之間的標(biāo)簽信息相互之間一一對應(yīng)的時候,在目標(biāo)函數(shù)中,把判別誤差、重構(gòu)誤差和分類誤差結(jié)合起來一起用K-SVD算法來學(xué)習(xí),更新字典和訓(xùn)練一個分類器。最后通過兩個手指心電信號數(shù)據(jù)庫(CYBHi,Surface ECG data)對本文的算法進行了性能測試,取得99%和100%的識別率。本文提出了基于手指心電信號分析的個體身份辨識算法研究,為手指ECG身份識別技術(shù)的實用化奠定了理論基礎(chǔ)和技術(shù)支撐。
[Abstract]:In the highly information-based modern society, with the rapid development of traffic, network and communication, the range of human activities is becoming wider and larger, and the security problems that people face are becoming more and more serious when the identity of individuals is realized. Biometrics (Biometrics) is a technique for identity identification using individual biometric features that are unique and stable over a long period of time, such as iris, fingerprints, faces and sounds, handwriting, gait, etc., with high accuracy and reliability. However, it also faces security problems, such as fake fingerprints, false iris, imitation of handwriting and so on. Therefore, a novel biometric identification technology with high security needs to be put forward urgently. Electrocardiogramme (ECG) contains unique identity information of human body, which is characterized by its high anti-counterfeiting ability. And in recent years, with the emergence of small volume, low energy consumption, no conductive glue and easy integration of ECG acquisition chips, ECG acquisition at the finger end has been realized, which can be widely used in family life. More convenient and quick implementation of individual identity recognition. In this paper, an individual identification algorithm based on finger ECG signal analysis is proposed. Firstly, the wavelet soft threshold and genetic algorithm are used to Denoise the finger ECG signal. Secondly, the sparse characteristic of the finger ECG signal is studied. In this paper, an algorithm of finger ECG identification based on sparse coding under KSVD PCA and an algorithm based on improved label consistent LC-KSVD are proposed. Finally, two finger ECG databases are used to verify the two algorithms. A large recognition rate was obtained. The main work of this paper is 1: 1. The mechanism of ECG signal generation, the characteristics of waveform, the development of ECG acquisition method and the identification algorithm based on finger ECG are described. The recognition evaluation index of ECG and two finger ECG databases are introduced. This paper provides a theoretical basis for the application of finger electrocardiogram in the field of individual identity recognition, and proposes an algorithm of finger ECG identity recognition based on sparse coding under K-SVD PCA. First of all, the finger ECG is denoised by wavelet soft threshold and genetic algorithm. After R peak detection, single cycle partition, normalization, P-QRS-T single cycle wave group is obtained and combined with finger electrocardiogram. The P-QRS-T wave group is extracted to form the feature vector and the dictionary template model is constructed. Then the redundant dictionary is trained by KSVD PCA and then each part of the feature vector is sparse encoded to realize the sparse representation on the dictionary. Finally, the performance of the algorithm is tested by using two ECG databases (CYBHiPSurface ECG data), and the recognition rates of 98.333% and 100% are obtained. A finger ECG identification algorithm based on improved tagged consistent LC-KSVD is proposed. First, the average single-period P-QRS-T wave group of finger ECG is extracted as the training sample, then an adaptive sub-dictionary and a adjustable class label are proposed to improve the label consistency of LC-KSVD(Label consistent KSVDU LC-KSVD, and then the improved LC-KSVD1 and LC-KSVD2 algorithms are used to complete the recognition. When the label information between the input signal and the dictionary atom corresponds one by one, in the objective function, the discriminant error, the reconstruction error and the classification error are combined to learn by the K-SVD algorithm. Update the dictionary and train a classifier. Finally, the performance of the proposed algorithm is tested by using two finger ECG database, CYBHiP Surface ECG data, and the recognition rates of 99% and 100% are obtained. In this paper, an individual identity identification algorithm based on finger ECG signal analysis is proposed, which lays a theoretical foundation and technical support for the practical application of finger ECG identification technology.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.6

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