技术领域technical field
本发明涉及利用近红外吸收监测指纹识别技术的方法，属于信息安全技术领域。The invention relates to a method for using near-infrared absorption to monitor fingerprint identification technology, and belongs to the technical field of information security.
背景技术Background technique
在高度信息化的现代社会，随着通信、网络、金融技术的高速发展，信息安全显示出前所未有的重要性。在日常生活以及司法、金融、安检、电子商务等诸多场合都需要更加可靠、稳定、不易伪造的识别技术。近年来随着科学技术与计算机的发展，一些生物特征识别技术如人脸识别、指纹识别、虹膜识别都得了普及，其中指纹识别以终身不变性、唯一性与便利性成为了目前应用最广泛的个人身份认证方法。然而，现阶段指纹识别技术仍然存在着巨大的安全隐患。其无法识别待提取特征的本体身份，缺乏对信息图像转化过程的实时监控，加之指纹裸露体表的特性，导致信息易被复制窃取。In the highly informationized modern society, with the rapid development of communication, network, and financial technology, information security has shown unprecedented importance. More reliable, stable and less forgery identification technology is needed in daily life and many occasions such as justice, finance, security inspection, e-commerce and so on. In recent years, with the development of science and technology and computers, some biometric identification technologies such as face recognition, fingerprint recognition, and iris recognition have been popularized. Among them, fingerprint recognition has become the most widely used method due to its lifelong invariance, uniqueness and convenience. Personal authentication method. However, there are still huge security risks in fingerprint identification technology at this stage. It cannot identify the identity of the ontology of the feature to be extracted, lacks real-time monitoring of the information image conversion process, and the characteristics of fingerprints that expose the body surface make the information easy to be copied and stolen.
1977年，Kaiser和Jobsis首次报告了血红蛋白和细胞色素在特定近红外区的吸收特性，并发现氧合血红蛋白和脱氧血红蛋白在760和850nm处有两处吸收峰，通过实验计量记录了红外光谱图数据。本发明以此为技术背景，并利用曲线拟合分析提取特征，通过验证活体身份以监测指纹识别技术。In 1977, Kaiser and Jobsis first reported the absorption characteristics of hemoglobin and cytochrome in the specific near-infrared region, and found that oxyhemoglobin and deoxyhemoglobin had two absorption peaks at 760 and 850nm, and recorded the infrared spectrogram data through experimental measurement . The present invention takes this as the technical background, uses curve fitting analysis to extract features, and monitors the fingerprint identification technology by verifying the identity of the living body.
发明内容Contents of the invention
针对背景技术的不足，本发明提供了利用近红外吸收监测指纹识别技术的方法，以提高防伪性，增加识别精确度。技术方案主要步骤如下：Aiming at the deficiency of the background technology, the present invention provides a method of using near-infrared absorption to monitor fingerprint recognition technology, so as to improve anti-counterfeiting and increase recognition accuracy. The main steps of the technical solution are as follows:
针对背景技术的不足，本发明提供了利用近红外吸收监测指纹识别技术的方法，以提高防伪性，增加识别精确度。技术方案主要步骤如下：Aiming at the deficiency of the background technology, the present invention provides a method of using near-infrared absorption to monitor fingerprint recognition technology, so as to improve anti-counterfeiting and increase recognition accuracy. The main steps of the technical solution are as follows:
针对背景技术的不足，本发明提供了利用近红外吸收监测指纹识别技术的方法，以提高防伪性，增加识别精确度。技术方案主要步骤如下：Aiming at the deficiency of the background technology, the present invention provides a method of using near-infrared absorption to monitor fingerprint recognition technology, so as to improve anti-counterfeiting and increase recognition accuracy. The main steps of the technical solution are as follows:
步骤1)参数初始化；Step 1) parameter initialization;
步骤2)进行有效区域估计，去除图像边缘和灰度变化不大的部分，将指纹图像划分成n×n(8≤n≤20)的方格，n表示划分格数；计算每块方格区间的灰度均值与方差，若二者满足条件，当前方格被定义为有效方格；连接所有有效方格并进行后处理得到指纹有效区域，记录长度l，l表示区域较短边长的长度；同时，将指纹有效区域范围信息传递给光纤探头，红外发射器自动调节发射角度以确保发射光进入有效区域；Step 2) Estimate the effective area, remove the edge of the image and the part with little change in gray level, divide the fingerprint image into n×n (8≤n≤20) grids, n represents the number of division grids; calculate each grid The gray mean and variance of the interval, if the two meet the conditions, the current grid is defined as a valid grid; connect all valid grids and perform post-processing to obtain the effective fingerprint area, record length l, l represents the length of the shorter side of the area At the same time, the information of the effective area of the fingerprint is transmitted to the optical fiber probe, and the infrared emitter automatically adjusts the emission angle to ensure that the emitted light enters the effective area;
步骤3)step 3)
(3-1)选定波长范围[400,1000]nm，控制光穿透厚度达到与指纹接触面相距b(0.3±0.05mm)处以确保上确界进入真皮层，b表示光穿透厚度；(3-1) Select the wavelength range [400,1000]nm, and control the light penetration thickness to reach b (0.3±0.05mm) away from the fingerprint contact surface to ensure that the supremum enters the dermis, and b represents the light penetration thickness;
(3-2)入射光穿过真皮层介质，则(3-2) The incident light passes through the medium of the dermis, then
S＝db·lS=db·l
-dIx＝k·Ix-dIx = k·Ix
S表示介质截面，db表示对b取微分，-dIx表示吸收的光强度，其中Ix表示辐射在介质截面S上的光强度，k表示光量子在与物质分子碰撞时被俘获的概率；S represents the cross section of the medium, db represents taking the differential for b, -dIx represents the light intensity absorbed, where Ix represents the light intensity radiated on the medium cross section S, and k represents the probability that the light quantum is captured when it collides with the material molecule;
假设a为任一分子存在有对光量子的俘获截面，则总俘获面积即有效面积为a·N，这里N为介质截面S中的分子数，故Assuming that a is any molecule that has a capture cross-section for light quanta, the total capture area, that is, the effective area, is a N, where N is the number of molecules in the medium cross-section S, so
k＝a·N/Sk=a·N/S
N＝NA·c·10-3·S·dbN＝NA ·c·10-3 ·S·db
NA为阿伏伽德罗常数，c表示物质的量浓度，单位为mol/L，故NA is Avogadro's constant, c represents the concentration of the substance, and the unit is mol/L, so
-dIx＝k·Ix＝(a·NA·c·10-3·S·Ix/S)·db＝(a·NA·c·Ix/1000)·db-dIx ＝k·Ix ＝(a ·NA·c·10-3 ·S·Ix /S)·db=(a ·NA·c·Ix /1000)·db
两边取积分Take points on both sides
有have
ln(I0/I)＝a·NA·c·b/1000ln(I0 /I)＝a·NA ·c·b/1000
其中，I0，I分别表示入射光强度与出射光强度；Among them, I0 and I represent the incident light intensity and the outgoing light intensity respectively;
两边取以lg为底的对数Logarithm to base lg on both sides
lg(I0/I)＝a·NA·c·b/(2.303×10-3)＝2.64×1020a·c·blg(I0 /I)=a·NA·c·b/(2.303×10-3 )=2.64×1020a ·c·b
吸收度K＝lg(I0/I)，2.64×1020a为摩尔吸收系数ε，则有Absorption K=lg(I0 /I), 2.64×10 20 a is the molar absorption coefficient ε, then
K＝εbcK=εbc
(3-3)在选定可见光与近红外线波长[400,1000]nm范围内，测得λi对应吸收度离散值Ki(i＝1，2，...，600)，其中下标i表示将区间均匀划分成600等份，λi表示在波长区间内下标i所对应的波长值，选定不同的波长进行测量，得到一组离散数据；(3-3) In the range of [400, 1000] nm of selected visible light and near-infrared wavelengths, the measured λi corresponds to the discrete value of absorbance Ki (i=1, 2, ..., 600), where the subscript i means that the interval is evenly divided into 600 equal parts, λi means the wavelength value corresponding to the subscripti in the wavelength interval, and different wavelengths are selected for measurement to obtain a set of discrete data;
步骤4)Step 4)
(4-1)用曲线拟合比拟吸收度离散值Ki与λi间的函数关系，计算机通过最小二乘法进行分析，并利用最小化误差的平方和寻找数据的最佳函数匹配，设函数为f，即(4-1) Use curve fitting to compare the functional relationship between the discrete values of absorbance Ki and λi , and the computer analyzes it through the least square method, and uses the square sum of the minimized errors to find the best function matching of the data, and the function for f, ie
K1＝f(λ1,λ2,...,λT)K1 ＝f(λ1 ,λ2 ,...,λT )
K2＝f(λ1,λ2,...,λT)K2 ＝f(λ1 ,λ2 ,...,λT )
K3＝f(λ1,λ2,...,λT)K3 ＝f(λ1 ,λ2 ,...,λT )
K4＝f(λ1,λ2,...,λT)K4 ＝f(λ1 ,λ2 ,...,λT )
......
KT＝f(λ1,λ2,...,λT)KT ＝f(λ1 ,λ2 ,...,λT )
其中T表示测量组数，要求T不小于100；Where T represents the number of measurement groups, and T is required to be not less than 100;
若λj为真值，由上述已知函数求出真值yj，若其测量值为yj*，则对应误差为σj＝yj-yj*(j＝1，2，...，n)，利用最小二乘法If λj is a true value, the true value yj is obtained from the known function above, and if its measured value is yj* , then the corresponding error is σj = yj -yj* (j=1, 2, .. ., n), using the least square method
得出最小误差的平方和，其中Pj表示各测量值的权重因子，利用最小误差的平方和拟合出最优函数表达式f(λ)；Get the sum of squares of the minimum error, where Pj represents the weight factor of each measurement value, and use the sum of squares of the minimum error to fit the optimal function expression f(λ);
(4-2)根据表达式求其极大值点以及相干特性并进行最大似然估计等数学分析，通过对比数据库成分确定是否为手指内血液。(4-2) Calculate the maximum value point and coherence characteristics according to the expression and perform mathematical analysis such as maximum likelihood estimation, and determine whether it is blood in the finger by comparing the database components.
有益效果Beneficial effect
(1)利用了手指真皮层浅部血管网薄且含血量大的生理结构，通过识别血管网的存在，有效认证手指的活体身份。(1) Utilizing the physiological structure of thin vascular network and large blood content in the superficial dermis of the finger, by identifying the existence of the vascular network, the living identity of the finger is effectively authenticated.
(2)准确排除假体身份，如目前市面上的指纹照片，指纹套，指纹干扰仪等。因测量区厚度级别为0.1mm，且规定了上确界与下确界，使人皮内部特性无法被其他材料仿制。(2) Accurately exclude prosthetic identities, such as fingerprint photos, fingerprint sleeves, fingerprint interferometers, etc. currently on the market. Because the thickness level of the measurement area is 0.1mm, and the supremum and infimum are stipulated, the internal characteristics of human skin cannot be imitated by other materials.
(3)目前近红外线光谱测定技术日趋成熟，傅里叶变换红外光谱仪、光栅扫描仪等都可成像，通过计算机实现对吸收度的测定，结合指纹识别成像，有效实现对身份的双重检验。(3) At present, the near-infrared spectrum measurement technology is becoming more and more mature. Fourier transform infrared spectrometers, raster scanners, etc. can be used for imaging, and the measurement of absorbance can be realized by computer. Combined with fingerprint identification and imaging, the double inspection of identity can be effectively realized.
(4)应用广泛，前景可观，适用于日常生活以及司法、金融、安检、电子商务等诸多领域。(4) It is widely used and has promising prospects, and is suitable for daily life and many fields such as justice, finance, security inspection, e-commerce and so on.
附图说明Description of drawings
图1是光的透射示意图；Figure 1 is a schematic diagram of light transmission;
图2是波长与吸收度理论函数；Fig. 2 is the theoretical function of wavelength and absorbance;
图3是近红外吸收监测指纹识别的建模流程图。Figure 3 is a modeling flow chart of near-infrared absorption monitoring fingerprint recognition.
具体实施方式detailed description
以下结合附图具体说明本发明技术方案。The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings.
一种利用近红外吸收监测指纹识别技术的方法，包含以下步骤：A method for utilizing near-infrared absorption to monitor fingerprint identification technology, comprising the following steps:
步骤1)参数初始化；Step 1) parameter initialization;
步骤2)Step 2)
进行有效区域估计，去除图像边缘和灰度变化不大的部分，将指纹图像划分成n×n(8≤n≤20)的方格，n表示划分格数；计算每块方格区间的灰度均值与方差，若二者满足条件，当前方格被定义为有效方格；连接所有有效方格并进行后处理得到指纹有效区域，记录长度l，l表示区域较短边长的长度；同时，将指纹有效区域范围信息传递给光纤探头，红外发射器自动调节发射角度以确保发射光进入有效区域；Estimate the effective area, remove the edge of the image and the part with little change in gray level, divide the fingerprint image into n×n (8≤n≤20) squares, n represents the number of divisions; calculate the gray area of each square interval Degree mean and variance, if both meet the conditions, the current grid is defined as a valid grid; connect all valid grids and perform post-processing to obtain the effective fingerprint area, record length l, l represents the length of the shorter side of the area; at the same time , the information of the effective area of the fingerprint is transmitted to the fiber optic probe, and the infrared emitter automatically adjusts the emission angle to ensure that the emitted light enters the effective area;
步骤3)(3-1)选定波长范围[400,1000]nm，控制光穿透厚度达到与指纹接触面相距b(0.3±0.05mm)处以确保上确界进入真皮层，b表示光穿透厚度；(3-2)入射光穿过真皮层介质，则Step 3) (3-1) select the wavelength range [400,1000]nm, control the thickness of light penetration to a distance of b (0.3±0.05mm) from the fingerprint contact surface to ensure that the supremum enters the dermis, b means light penetration penetration thickness; (3-2) the incident light passes through the dermis medium, then
S＝db·lS=db·l
-dIx＝k·Ix-dIx = k·Ix
S表示介质截面，db表示对b取微分，-dIx表示吸收的光强度，其中Ix表示辐射在介质截面S上的光强度，k表示光量子在与物质分子碰撞时被俘获的概率；S represents the cross section of the medium, db represents taking the differential for b, -dIx represents the light intensity absorbed, where Ix represents the light intensity radiated on the medium cross section S, and k represents the probability that the light quantum is captured when it collides with the material molecule;
假设a为任一分子存在有对光量子的俘获截面，则总俘获面积即有效面积为a·N，这里N为介质截面S中的分子数，故Assuming that a is any molecule that has a capture cross-section for light quanta, the total capture area, that is, the effective area, is a N, where N is the number of molecules in the medium cross-section S, so
k＝a·N/Sk=a·N/S
N＝NA·c·10-3·S·dbN＝NA ·c·10-3 ·S·db
NA为阿伏伽德罗常数，c表示物质的量浓度，单位为mol/L，故NA is Avogadro's constant, c represents the concentration of the substance, and the unit is mol/L, so
-dIx＝k·Ix＝(a·NA·c·10-3·S·Ix/S)·db＝(a·NA·c·Ix/1000)·db-dIx ＝k·Ix ＝(a ·NA·c·10-3 ·S·Ix /S)·db=(a ·NA·c·Ix /1000)·db
两边取积分Take points on both sides
有have
ln(I0/I)＝a·NA·c·b/1000ln(I0 /I)＝a·NA ·c·b/1000
其中，I0，I分别表示入射光强度与出射光强度；Among them, I0 and I represent the incident light intensity and the outgoing light intensity respectively;
两边取以lg为底的对数Logarithm to base lg on both sides
lg(I0/I)＝a·NA·c·b/(2.303×10-3)＝2.64×1020a·c·blg(I0 /I)=a·NA·c·b/(2.303×10-3 )=2.64×1020a ·c·b
吸收度K＝lg(I0/I)，2.64×1020a为摩尔吸收系数ε，则有Absorption K=lg(I0 /I), 2.64×10 20 a is the molar absorption coefficient ε, then
K＝εbcK=εbc
(3-3)在选定可见光与近红外线波长[400,1000]nm范围内，测得λi对应吸收度离散值Ki(i＝1，2，...，600)，其中下标i表示将区间均匀划分成600等份，λi表示在波长区间内下标i所对应的波长值，选定不同的波长进行测量，得到一组离散数据；(3-3) In the range of [400, 1000] nm of selected visible light and near-infrared wavelengths, the measured λi corresponds to the discrete value of absorbance Ki (i=1, 2, ..., 600), where the subscript i means that the interval is evenly divided into 600 equal parts, λi means the wavelength value corresponding to the subscripti in the wavelength interval, and different wavelengths are selected for measurement to obtain a set of discrete data;
步骤4)Step 4)
(4-1)用曲线拟合比拟吸收度离散值Ki与λi间的函数关系，计算机通过最小二乘法进行分析，并利用最小化误差的平方和寻找数据的最佳函数匹配，设函数为f，即(4-1) Use curve fitting to compare the functional relationship between the discrete values of absorbance Ki and λi , and the computer analyzes it through the least square method, and uses the square sum of the minimized errors to find the best function matching of the data, and the function for f, ie
K1＝f(λ1,λ2,...,λT)K1 ＝f(λ1 ,λ2 ,...,λT )
K2＝f(λ1,λ2,...,λT)K2 ＝f(λ1 ,λ2 ,...,λT )
K3＝f(λ1,λ2,...,λT)K3 ＝f(λ1 ,λ2 ,...,λT )
K4＝f(λ1,λ2,...,λT)K4 ＝f(λ1 ,λ2 ,...,λT )
......
KT＝f(λ1,λ2,...,λT)KT ＝f(λ1 ,λ2 ,...,λT )
其中T表示测量组数，要求T不小于100；Where T represents the number of measurement groups, and T is required to be not less than 100;
若λj为真值，由上述已知函数求出真值yj，若其测量值为yj*，则对应误差为σj＝yj-yj*(j＝1，2，...，n)，利用最小二乘法If λj is a true value, the true value yj is obtained from the known function above, and if its measured value is yj* , then the corresponding error is σj = yj -yj* (j=1, 2, .. ., n), using the least square method
得出最小误差的平方和，其中Pj表示各测量值的权重因子，利用最小误差的平方和拟合出最优函数表达式f(λ)；Get the sum of squares of the minimum error, where Pj represents the weight factor of each measurement value, and use the sum of squares of the minimum error to fit the optimal function expression f(λ);
(4-2)根据表达式求其极大值点以及相干特性并进行最大似然估计等数学分析，通过对比数据库成分确定是否为手指内血液。(4-2) Calculate the maximum value point and coherence characteristics according to the expression and perform mathematical analysis such as maximum likelihood estimation, and determine whether it is blood in the finger by comparing the database components.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710243110.4ACN107092879B (en) | 2017-04-14 | 2017-04-14 | A method for monitoring fingerprint identification technology using near-infrared absorption |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710243110.4ACN107092879B (en) | 2017-04-14 | 2017-04-14 | A method for monitoring fingerprint identification technology using near-infrared absorption |
| Publication Number | Publication Date |
|---|---|
| CN107092879Atrue CN107092879A (en) | 2017-08-25 |
| CN107092879B CN107092879B (en) | 2020-10-02 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710243110.4AActiveCN107092879B (en) | 2017-04-14 | 2017-04-14 | A method for monitoring fingerprint identification technology using near-infrared absorption |
| Country | Link |
|---|---|
| CN (1) | CN107092879B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232152A (en)* | 2020-09-30 | 2021-01-15 | 墨奇科技（北京）有限公司 | Non-contact fingerprint identification method and device, terminal and storage medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030016345A1 (en)* | 2001-07-19 | 2003-01-23 | Akio Nagasaka | Finger identification apparatus |
| WO2004068394B1 (en)* | 2003-01-21 | 2004-09-23 | Atmel Grenoble Sa | Person recognition method and device |
| CN101953689A (en)* | 2009-07-16 | 2011-01-26 | 索尼公司 | Biological authentication apparatus |
| CN203444497U (en)* | 2013-08-29 | 2014-02-19 | 深圳市中控生物识别技术有限公司 | Finger vein and fingerprint acquisition integrated apparatus |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030016345A1 (en)* | 2001-07-19 | 2003-01-23 | Akio Nagasaka | Finger identification apparatus |
| WO2004068394B1 (en)* | 2003-01-21 | 2004-09-23 | Atmel Grenoble Sa | Person recognition method and device |
| CN101953689A (en)* | 2009-07-16 | 2011-01-26 | 索尼公司 | Biological authentication apparatus |
| CN203444497U (en)* | 2013-08-29 | 2014-02-19 | 深圳市中控生物识别技术有限公司 | Finger vein and fingerprint acquisition integrated apparatus |
| Title |
|---|
| KEVIN GILL等: "Quality-Assured Fingerprint Image Enhancement and Extraction using Hyperspectral Imaging", 《IEEE》* |
| 李洵等: "布渣叶药材近红外指纹图谱的聚类分析", 《广东药学院学报》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232152A (en)* | 2020-09-30 | 2021-01-15 | 墨奇科技（北京）有限公司 | Non-contact fingerprint identification method and device, terminal and storage medium |
| Publication number | Publication date |
|---|---|
| CN107092879B (en) | 2020-10-02 |
| Publication | Publication Date | Title |
|---|---|---|
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