發(fā)布時(shí)間：2018-11-26 07:37

【摘要】：隨著智能化、信息化與社會(huì)、生活各方面的不斷融合、交互,互聯(lián)網(wǎng)和物聯(lián)網(wǎng)的日益普及,信息和系統(tǒng)的安全性問題成為備受矚目的關(guān)鍵性問題,身份認(rèn)證作為解決安全性問題重要手段之一,受到廣泛的關(guān)注。生物識(shí)別技術(shù)便應(yīng)用而生,掌紋識(shí)別具有特征豐富穩(wěn)定具有可靠性和唯一性、用戶易接受、易獲取等優(yōu)勢,近幾年已成為人機(jī)交互和模式識(shí)別等領(lǐng)域的重點(diǎn)研究對(duì)象。傳統(tǒng)的掌紋特征提取和識(shí)別技術(shù)在識(shí)別精度和速度方面仍存在著許多不足,特征提取和匹配至今為止仍是學(xué)者們研究的重點(diǎn),需要對(duì)其進(jìn)行進(jìn)一步的改進(jìn)和性能的提升。本文通過閱讀大量掌紋識(shí)別相關(guān)文獻(xiàn),了解國內(nèi)外研究及發(fā)展現(xiàn)狀,歸納總結(jié)、對(duì)比分析傳統(tǒng)算法的優(yōu)劣勢,針對(duì)特征提取和模式匹配問題,采用梯度方向直方圖特征(HOG),并結(jié)合分區(qū)的分塊二值模式和壓縮感知算法將其用于掌紋識(shí)別方法。本論文的主要工作如下:(1)提出基于分區(qū)的分塊二值模式與梯度方向直方圖特征的掌紋識(shí)別方法。該方法主要采用的是紋理特征和邊緣特征的融合特征,充分利用二者互補(bǔ)的特性來提升算法的性能。首先,對(duì)原掌紋圖片進(jìn)行預(yù)處理獲取掌紋感興趣區(qū)域。然后,分別提取掌紋R0I區(qū)域的分區(qū)MB-LBP特征和HOG特征。將得到的分區(qū)MB-LBP特征和HOG特征串聯(lián)起來,得到圖片融合后的特征。最后,使用最近鄰分類器對(duì)圖片進(jìn)行分類,得到識(shí)別結(jié)果。使用北京交通大學(xué)的掌紋庫進(jìn)行實(shí)驗(yàn)后,通過與傳統(tǒng)算法進(jìn)行對(duì)比分析,本文的算法在識(shí)別精度上具有相對(duì)的優(yōu)勢。(2)提出基于壓縮感知與梯度方向直方圖特征的掌紋識(shí)別方法。首先,對(duì)原掌紋圖片進(jìn)行預(yù)處理得到掌紋感興趣區(qū)域,提取R0I區(qū)域的HOG特征,將訓(xùn)練樣本的HOG特征作為稀疏表示的過完備字典。然后通過COMP算法求解圖像在過完備字典上的稀疏表示,求得一組最優(yōu)稀疏系數(shù)重構(gòu)每一個(gè)圖像,最后計(jì)算測試樣本圖像HOG特征矩陣與各類重構(gòu)圖像的最小殘差得出分類結(jié)果。使用北京交通大學(xué)的掌紋庫進(jìn)行實(shí)驗(yàn),表明本文算法不局限于小樣本的情況,具有較高的識(shí)別性能。
[Abstract]:With the continuous integration and interaction of intelligence, information and society, various aspects of life, and the growing popularity of the Internet of things and the Internet of things, the security of information and systems has become a key issue that has attracted much attention. As one of the most important methods to solve the security problem, identity authentication has been paid more and more attention. The technology of biometrics has been applied, palmprint recognition has the advantages of rich, stable, reliability and uniqueness, easy to accept by users and easy to obtain. In recent years, palmprint recognition has become an important research object in the fields of human-computer interaction and pattern recognition. Traditional palmprint feature extraction and recognition techniques still have many shortcomings in recognition accuracy and speed. Feature extraction and matching are still the focus of scholars' research so far, it needs further improvement and performance improvement. In this paper, we read a large number of palmprint recognition related documents, understand the domestic and foreign research and development status, summarize, compare and analyze the advantages and disadvantages of the traditional algorithm, aiming at the problem of feature extraction and pattern matching, we adopt gradient direction histogram feature (HOG),. Combined with partitioned binary pattern and compression sensing algorithm, it is used in palmprint recognition. The main work of this thesis is as follows: (1) A method of palmprint recognition based on partitioned binary pattern and gradient direction histogram feature is proposed. The method mainly adopts the fusion features of texture feature and edge feature, and makes full use of the complementary characteristics of the two features to improve the performance of the algorithm. Firstly, the original palmprint image is preprocessed to obtain the region of palmprint interest. Then, the partition MB-LBP features and HOG features of palmprint R0I region are extracted. The segmented MB-LBP features and the HOG features are concatenated to obtain the features after image fusion. Finally, the nearest neighbor classifier is used to classify the images and the recognition results are obtained. The palmprint database of Beijing Jiaotong University is used to carry out the experiment, and by comparing with the traditional algorithm, The algorithm in this paper has relative advantages in recognition accuracy. (2) A palmprint recognition method based on compression perception and gradient direction histogram features is proposed. Firstly, the region of palmprint interest is obtained by preprocessing the original palmprint image, and the HOG feature of R0I region is extracted. The HOG feature of the training sample is regarded as an overcomplete dictionary with sparse representation. Then, the sparse representation of images in overcomplete dictionaries is solved by COMP algorithm, and a set of optimal sparse coefficients are obtained to reconstruct each image. Finally, the classification results are obtained by calculating the HOG feature matrix of test sample images and the minimum residuals of various reconstructed images. By using palmprint database of Beijing Jiaotong University, it is shown that the proposed algorithm is not limited to small samples and has high recognition performance.
【學(xué)位授予單位】：內(nèi)蒙古農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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本文編號(hào)：2357813