【摘要】：隨著近些年人工智能與圖像識別領(lǐng)域呈現(xiàn)出的越來越火熱的趨勢以及智能電子設(shè)備數(shù)量和普及率的爆發(fā)式增長,讓生物識別技術(shù)真正的融入到了人們的生活之中。虹膜識別技術(shù)快速發(fā)展的三十年來,國內(nèi)外專家學(xué)者經(jīng)過不懈的努力,已經(jīng)提出并完善了許多經(jīng)典的算法。但是無論識別技術(shù)如何先進,如果不能對偽造攻擊手段做出有效的防御也不能被稱作是合格的識別技術(shù)。本文主要針對已有基于瞳孔對光反射特性的活體檢測算法的所存在的局限性結(jié)合了虹膜紋理變化檢測共同組成分類特征來對相應(yīng)攻擊模型進行防御。并根據(jù)該算法無法很好防御的移動智能設(shè)備等新生攻擊手段提出了基于雙紅外波段的活體虹膜檢測算法。豐富完善了對于不同攻擊手段的防御方法。本文的創(chuàng)新性工作可以概括如下:(1)提出了一種結(jié)合虹膜紋理和瞳孔反射特性的活體檢測算法,在原有算法通過對瞳孔光反射特性檢測的基礎(chǔ)上結(jié)合對虹膜紋理特征的檢測來防御通過位移變化等方法模擬瞳孔收縮變化的模型攻擊。在該算法中為了對瞳孔部分更準確的進行分割提出了步進式自適應(yīng)閾值選取算法,相比固定閾值的分割具有更好的魯棒性,為后續(xù)的定位精準度提供保障。提出了改進的基于Hough變換的定位算法在原有基礎(chǔ)上結(jié)合了形態(tài)學(xué)質(zhì)心法,對瞳孔和虹膜在感興區(qū)域內(nèi)進行定位,降低了搜索空間,相較幾何算法提升了定位準確度的同時大大縮短了算法的耗時,提高了效率。(2)提出了一種基于雙紅外波段的活體虹膜檢測算法,通過活體人眼中血管內(nèi)組織與偽造樣本在不同紅外波段的吸收反射率的差異來進行真?zhèn)螀^(qū)分。通過對活體和偽造樣本中的血管紋理特征在不同紅外波段下的成像清晰程度進行統(tǒng)計實驗,選取活體人眼與偽造樣本前后紋理特征數(shù)量變化差異性最大的兩個紅外波段作為算法中的兩個對照波段。該方法可以很好的對前一算法無法很好防御的移動智能設(shè)備等新生攻擊手段進行防御。針對上述方法,本文在CASIA v1.0和v2.0虹膜庫及所采集虹膜庫上進行了充分的實驗,進一步驗證了所提出方法具有一定的有效性。
[Abstract]:With the increasing trend of artificial intelligence and image recognition in recent years and the explosive growth of the number and popularity of intelligent electronic devices, biometric technology has really been integrated into people's lives. With the rapid development of iris recognition technology, experts and scholars at home and abroad have put forward and improved many classical algorithms through unremitting efforts. But no matter how advanced the recognition technology is, it can not be called qualified recognition technology if it can not effectively defend against forgery attacks. In this paper, aiming at the limitations of the existing in vivo detection algorithms based on pupil reflection characteristics, combined with iris texture change detection to form classification features to defend the corresponding attack model. According to the new attack methods such as mobile intelligent devices, which can not be well protected by this algorithm, a living iris detection algorithm based on double infrared band is proposed. It enriches and perfects the defense methods for different attack methods. The innovative work of this paper can be summarized as follows: (1) an in vivo detection algorithm based on iris texture and pupil reflection is proposed. Based on the detection of pupil light reflection characteristics and the detection of iris texture features, the original algorithm defends the model attack of simulating pupil contraction change by displacement change and so on. In order to segment the pupil more accurately, a step-by-step adaptive threshold selection algorithm is proposed in this algorithm, which has better robustness than the fixed threshold segmentation, and provides a guarantee for the subsequent positioning accuracy. An improved location algorithm based on Hough transform is proposed, which combines the morphological centroids method to locate the pupils and iris in the sensitive region, which reduces the search space. Compared with the geometric algorithm, it not only improves the positioning accuracy, but also greatly shortens the time consuming and improves the efficiency of the algorithm. (2) A living iris detection algorithm based on double infrared band is proposed. The difference of absorption reflectivity between intravascular tissue and forged samples in living human eyes in different infrared bands was used to distinguish the true and false. Through statistical experiments on the imaging clarity of vascular texture features in living and forged samples in different infrared bands, Two infrared bands with the greatest difference in the number of texture features before and after living human eyes and forged samples are selected as the two control bands in the algorithm. This method can defend against new attacks such as mobile intelligent devices, which can not be well protected by the previous algorithm. In view of the above methods, sufficient experiments have been carried out on CASIA v1.0 and v2.0 rainbow film libraries and the collected rainbow film libraries, and the effectiveness of the proposed method has been further verified.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 鐵興華;李長棟;孫建軍;荔志云;;不同光線下研究對象的年齡、性別與瞳孔變化的臨床研究[J];中國民康醫(yī)學(xué);2016年14期

2 ;瞳孔大小變化之謎[J];中國眼鏡科技雜志;2014年24期

3 王月明;趙士偉;張如彩;;基于人機交互的虹膜圖像采集系統(tǒng)設(shè)計[J];中國安防;2014年17期

4 鄭英娟;張有會;王志巍;張靜;范勝娟;;基于八方向Sobel算子的邊緣檢測算法[J];計算機科學(xué);2013年S2期

5 雷登峰;鄭群輝;;一種精確的相機景深計算方法[J];信息技術(shù);2013年08期

6 宋輝;陳浩杰;張立華;;基于灰度直方圖最小跨度閾值法的瞳孔分割[J];中國印刷與包裝研究;2011年02期

7 田啟川;張潤生;;生物特征識別綜述[J];計算機應(yīng)用研究;2009年12期

8 王一丁;蔣小森;;基于梯度增強的新聞字幕分割算法[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2009年08期

9 江明;劉輝;黃歡;;圖像二值化技術(shù)的研究[J];軟件導(dǎo)刊;2009年04期

10 蔣婷;譚躍剛;劉泉;;基于SOBEL算子的圖像清晰度評價函數(shù)研究[J];計算機與數(shù)字工程;2008年08期

相關(guān)博士學(xué)位論文 前1條

1 何孝富;活體虹膜識別的關(guān)鍵技術(shù)研究[D];上海交通大學(xué);2007年

相關(guān)碩士學(xué)位論文 前6條

1 張羝;基于多光譜的手背靜脈活體檢測[D];北方工業(yè)大學(xué);2016年

2 劉博;結(jié)構(gòu)相似性圖像質(zhì)量評價方法研究[D];大連理工大學(xué);2012年

3 孫業(yè)超;虹膜識別預(yù)處理算法研究[D];山東大學(xué);2011年

4 白濤;虹膜識別預(yù)處理和特征識別算法研究[D];吉林大學(xué);2009年

5 駱麗;實時虹膜圖像質(zhì)量評估的算法研究與實現(xiàn)[D];電子科技大學(xué);2008年

6 韓瑜;虹膜圖像的質(zhì)量評估方法研究[D];哈爾濱工程大學(xué);2006年

，

本文編號：2484050