WO2017107406A1 - 利用汗腺位置信息的信息识别方法及系统 - Google Patents

利用汗腺位置信息的信息识别方法及系统 Download PDF

Info

Publication number
WO2017107406A1
WO2017107406A1 PCT/CN2016/085664 CN2016085664W WO2017107406A1 WO 2017107406 A1 WO2017107406 A1 WO 2017107406A1 CN 2016085664 W CN2016085664 W CN 2016085664W WO 2017107406 A1 WO2017107406 A1 WO 2017107406A1
Authority
WO
WIPO (PCT)
Prior art keywords
sweat gland
information
position information
sweat
ridge line
Prior art date
Application number
PCT/CN2016/085664
Other languages
English (en)
French (fr)
Inventor
金虎林
Original Assignee
金虎林
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 金虎林 filed Critical 金虎林
Publication of WO2017107406A1 publication Critical patent/WO2017107406A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1365—Matching; Classification
    • G06V40/1371—Matching features related to minutiae or pores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques

Definitions

  • the invention belongs to the technical field of information recognition, and relates to an information recognition method, in particular to an information recognition method using sweat gland position information. Meanwhile, the present invention also relates to an information recognition system using sweat gland position information.
  • Methods for identity recognition through biometric identification include fingerprint recognition, iris recognition, vein recognition, face recognition, voice recognition, palmprint recognition, and auricle recognition.
  • biometric identification includes fingerprint recognition, iris recognition, vein recognition, face recognition, voice recognition, palmprint recognition, and auricle recognition.
  • many devices that extract biometric features are not suitable for small portable devices such as mobile phones because of their size or cost.
  • the fingerprint recognition input device has been developed to be small enough to be directly embedded in a handheld mobile device such as a mobile phone, but the size of the input device is too small to extract the required fingerprint feature points, and the phenomenon of rejection is often occurred during the recognition process. The probability is high.
  • the technical problem to be solved by the present invention is to provide an information recognition method using position information of sweat glands, which can improve the recognition accuracy and effectively reduce the false recognition rate.
  • the present invention also provides an information recognition system that utilizes sweat gland position information, which can improve recognition accuracy and effectively reduce false recognition rate.
  • An information recognition method using sweat gland position information includes:
  • Step S1 extracting position information of the sweat gland from the fingerprint image and storing the information
  • the sweat gland position information is extracted and stored.
  • the sweat glands located on the same ridge line try to store the same group to store information.
  • the type of sweat gland is divided into sweat gland information at the ridge end, sweat gland information at the bifurcation point, and the continuous ridge line. Sweat gland information; classified and stored sweat gland information through the above three basic types;
  • Step S2 acquiring a newly input fingerprint image
  • Step S3 comparing the sweat gland information in the newly entered fingerprint image with the stored fingerprint sweat gland to determine whether it is the same, and comparing the position information of the sweat gland;
  • the sweat gland position information located on the same ridge line is compared; as a feature, whether the sweat gland position information is used to identify whether it is a person;
  • the sweat gland position information located in each of the different ridges is compared; thereby, whether the identification is based on the sweat gland position information;
  • the sweat glands located on the same ridge line are connected to generate a graph; this is characterized by whether the sweat gland position information is recognized by the person.
  • An information recognition method using sweat gland position information includes:
  • Step S1 storing the location information of the sweat gland in a storage module
  • Step S2 acquiring a newly entered fingerprint image, and extracting sweat gland information
  • Step S3 Comparing the sweat gland information in the newly entered fingerprint image with the stored fingerprint sweat gland position information to determine whether they are the same.
  • step S1 the position information of the sweat glands is extracted from the fingerprint image and stored; the sweat gland position information is extracted and stored, and the sweat glands located on the same ridge line try to share the same group to store information.
  • step S1 the position information of the sweat gland is extracted from the fingerprint image and stored; the type of sweat gland is divided into sweat gland information located at the ridge terminal, sweat gland information located at the bifurcation point, and located in the continuous ridge. Sweat gland information of the line; the sweat gland information is stored by the above three basic types.
  • step S3 in the comparison process, the sweat gland position information located on the same ridge line is compared; thereby, whether the recognition is based on the sweat gland position information.
  • step S3 in the alignment process, they are located in different ridges.
  • the sweat gland position information of the line is compared; it is characterized by whether the sweat gland position information is recognized by the person.
  • the angle between the sweat glands and the adjacent angle are extracted by the adjacent sweat gland position information; thereby, whether the identification is based on the sweat gland position information.
  • step S3 in the comparison process, the sweat glands located on the same ridge line are connected to generate a graph; thereby, it is characterized whether the sweat gland position information is recognized by the sweat gland position information.
  • An information recognition system utilizing sweat gland position information comprising:
  • a sweat gland position information database for storing position information of sweat glands
  • a storage module for storing location information of sweat glands
  • a sweat gland information extraction module for acquiring a newly entered fingerprint image and extracting sweat gland information
  • the judging module is configured to compare the sweat gland information in the newly entered fingerprint image with the stored fingerprint sweat gland position information to determine whether they are the same.
  • the sweat gland information extraction module is further configured to extract position information of sweat glands from the fingerprint image and store the same in the storage module; extract sweat gland position information and store the same in the same process.
  • the sweat glands of the ridge line try to do the same group to store information; the type of sweat gland is divided into sweat gland information at the end of the ridge line, sweat gland information at the bifurcation point, and sweat gland information located in the continuous ridge line; classification by the above three basic types Store sweat gland information;
  • the judging module compares the sweat gland position information located in the same ridge line during the comparison process; and whether the identification is based on the sweat gland position information;
  • the judging module compares the sweat gland position information located in different ridges during the comparison process; and whether the identification is based on the sweat gland position information;
  • the angle between the sweat glands and the adjacent angle are extracted by the adjacent sweat gland position information; and the identification is based on the sweat gland position information;
  • the judging module connects the sweat glands located in the same ridge line during the comparison process to generate a graph; thereby identifying whether the sweat gland position information is self-identified by the sweat gland position information.
  • the invention has the beneficial effects that the information recognition method and system using the sweat gland position information proposed by the invention can improve the recognition precision and effectively reduce the false recognition rate.
  • Figure 1 is a general fingerprint image of a sweat gland.
  • Fig. 2 is a diagram showing sweat glands and fingerprint images which are illustrated for convenience of explanation of the present invention.
  • Figure 3 is a table of sweat gland position information.
  • Figure 4 is a type of fingerprint recovered by stored sweat gland position information.
  • Fig. 5 is a schematic diagram of a method of extracting angle information by sweat gland information in the same ridge line.
  • the present invention discloses an information recognition method using sweat gland position information, and the identification method includes:
  • Step S1 extracting position information of the sweat gland from the fingerprint image and storing it;
  • the sweat gland position information is extracted and stored.
  • the sweat glands located on the same ridge line try to store the same group to store information.
  • the type of sweat gland is divided into sweat gland information at the ridge end, sweat gland information at the bifurcation point, and the continuous ridge line. Sweat gland information; classified and stored sweat gland information through the above three basic types;
  • Step S2 acquiring a newly entered fingerprint image
  • Step S3 comparing the sweat gland information in the newly entered fingerprint image with the stored fingerprint sweat gland to determine whether it is the same, and comparing the sweat gland position information;
  • the sweat gland position information located on the same ridge line is compared; as a feature, whether the sweat gland position information is used to identify whether it is a person;
  • the sweat gland position information located in each of the different ridges is compared; thereby, whether the identification is based on the sweat gland position information;
  • the sweat glands located on the same ridge line are connected to generate a graph; It is characterized by whether the sweat gland position information is identified by the person.
  • the present invention also discloses an information recognition system using sweat gland position information; the identification system includes: a storage module, a sweat gland information extraction module, Judging module.
  • the storage module is configured to store position information of the sweat gland; the sweat gland information extraction module is configured to acquire a newly entered fingerprint image and extract sweat gland information.
  • the judging module is configured to compare the sweat gland information in the newly entered fingerprint image with the stored fingerprint sweat gland position information to determine whether they are the same.
  • the sweat gland information extraction module is further configured to extract position information of the sweat gland from the fingerprint image and store it in the storage module; extract sweat gland position information and store the same sweat group in the same ridge line during the storage process
  • the information is stored; the type of sweat gland is divided into sweat gland information at the end of the ridge line, sweat gland information at the bifurcation point, and sweat gland information located in the continuous ridge line; the sweat gland information is classified and stored by the above three basic types.
  • the judging module compares the sweat gland position information located in the same ridge line during the comparison process; thereby, whether the identification is based on the sweat gland position information.
  • the judging module compares the sweat gland position information located in the respective different ridges during the comparison process; thereby, whether the identification is based on the sweat gland position information.
  • the judging module extracts the angle and the adjacent angle between the sweat glands through the adjacent sweat gland position information during the comparison process; thereby, whether the identification is based on the sweat gland position information.
  • the judging module connects the sweat glands located in the same ridge line during the comparison process to generate a graph; thereby identifying whether the sweat gland position information is self-identified by the sweat gland position information.
  • Figure 1 is a general fingerprint image of a sweat gland, in which it can be seen that the sweat glands of the fingerprint form a ridge. The point at which the ridge ends is the end point, and the point at which the rip line ends is the bifurcation point.
  • Fig. 2 is an explanatory diagram of the extracted sweat gland position information illustrated for explaining the present invention.
  • 100 denotes a window of the fingerprint image sensor, and 110 and 120 each represent an X-axis value (110) and a Y-axis value (120) of positional information of the sweat gland.
  • R1, R2, R3 are ridges, S1, S2, ... S16 are sweat glands on the continuous ridgeline, B1 is the sweat gland at the bifurcation point, and E1 is the sweat gland at the end point.
  • the position information of S3 is changed from the origin (130) of the sensor to the X-axis (x') and the change value of the Y-axis.
  • (y') indicates that the X and Y values of the origin (130) of the sensor are (0, 0), respectively, and the positional information of S3 is (x', y').
  • Tables 1 to 3 are sweat gland position information tables. That is, the sweat gland position information extracted in FIG. 2 is stored in a table manner as shown in Tables 1 to 3.
  • a ridge line is saved by a sweat gland position information table.
  • the number of tables is determined by the number of ridges.
  • the ridge lines are represented as R1 to Rk, except for the sweat glands at the end point and the bifurcation point, that is, the sweat glands on the ridge line are represented as S1 to Sn, the sweat glands at the bifurcation point are represented as B1 to Bm, and the sweat glands at the end point are expressed as E1 to Ep.
  • ridge information Rk k is an ordinal number indicating the total number of ridge lines including sweat glands.
  • n of Sn represents the total number of sweat glands except for the sweat glands located at the end point and the bifurcation point.
  • m of Bm represents the total sweat gland at the bifurcation point.
  • the number, the p of Ep represents the total number of sweat glands at the end point. Therefore, the total number of sweat glands extracted in the fingerprint image sensor is n+m+p, which is the sum of all sweat glands.
  • the positions of Sn, Bm, and Ep can be extracted as changes from the origin (130) to the X-axis (110) and Y-axis (120) of the sensor in FIG.
  • FIG. 3 is a schematic diagram of restoring a fingerprint image by the saved sweat gland information.
  • the graph shown in Fig. 4 can be obtained by linearly connecting the sweat gland position information of the same ridge line by using the sweat gland position information stored as shown in Fig. 3.
  • the generated graph can be used for user authentication by methods such as pattern recognition.
  • Fig. 4 is a view showing an example of a method of extracting angle information by using sweat gland position information on the same ridge line.
  • the position of the sweat glands on the same ridge line can be extracted by straight lines to extract the angle 510 between the angle 540 of the sweat gland and the angle 510 of the 550 sweat gland and the adjacent angle information between the X-axis and the direction of the sweat gland position, and the adjacent sweat gland distance information. Therefore, the saved sweat gland position information can be directly applied to the user authentication and recognition based on the traditional fingerprint feature point method.
  • Fig. 5 shows the case where the fingerprint of Fig. 4 is rotated by a certain angle. Even after the fingerprint is rotated by a certain angle, the angle 500 of the 540 sweat gland in Fig. 4 and the angle 500' of the 540 sweat gland of Fig. 5 maintain the same value. The angle 510 of the 550 sweat gland in Fig. 4 and the sweat gland angle 510' of Fig. 5 also maintain the same value. The rotated image is easier to determine whether it is the same image because the two sweat gland information maintains the same angle value compared to the original image.
  • An information recognition method using sweat gland position information includes:
  • Step S1 The position information of the sweat glands is stored in a storage module.
  • the position information of the sweat glands is extracted from the fingerprint image and stored; the sweat gland position information is extracted and stored, and the sweat glands located on the same ridge line are tried to store the same group.
  • the type of sweat gland is divided into sweat gland information at the ridge end, sweat gland information at the bifurcation point, and sweat gland information located in the continuous ridge line; the sweat gland information is classified and stored by the above three basic types.
  • Step S2 Acquire a newly entered fingerprint image and extract sweat gland information.
  • Step S3 The sweat gland information in the newly entered fingerprint image is compared with the stored fingerprint sweat gland position information to determine whether they are the same.
  • the sweat gland position information located on the same ridge line is compared; it is characterized by whether the sweat gland position information is recognized by the sweat gland position information.
  • the sweat gland position information at the respective different ridges is compared; as a feature, whether the sweat gland position information is used to identify whether or not the person is present.
  • the angle between the sweat glands and the adjacent angles are extracted by the adjacent sweat gland position information; thereby, whether the identification is based on the sweat gland position information.
  • the sweat glands located on the same ridge line are connected to generate a graph; this is characterized by whether the sweat gland position information is recognized by the person.
  • the information recognition method and system using the sweat gland position information proposed by the present invention can improve the recognition accuracy and effectively reduce the false recognition rate.

Abstract

一种利用汗腺位置信息的信息识别方法及系统，所述识别方法包括：步骤S1、将汗腺的位置信息储存于一存储模块；从指纹图像中提取汗腺的位置信息并储存；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；步骤S2、获取新录入的指纹图像，并提取汗腺信息；步骤S3、对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。可提高识别精度，有效降低误识别率。

Description

利用汗腺位置信息的信息识别方法及系统 技术领域
本发明属于信息识别技术领域，涉及一种信息识别方法，尤其涉及一种利用汗腺位置信息的信息识别方法；同时，本发明还涉及一种利用汗腺位置信息的信息识别系统。
背景技术
通过生物识别进行身份确认的方法有指纹识别，虹膜识别，静脉识别，人脸识别，声音识别，掌纹识别和耳廓识别等多种多样。但提取生物识别特征的设备因为其尺寸大小或成本原因很多都不太适合用于手机等小型携带设备上。
指纹识别输入设备已发展成足够小而直接嵌入到手机等手持移动设备上，但其输入设备的尺寸过小而无法提取所需的指纹特征点而识别过程中经常发生拒绝识别的现象即发生FRR概率很高。
即使通过手机上的前端摄像头进行人脸识别过程中，因为对周围照明环境的影响及摄像头的分辨率等因素识别过程经常发生拒绝识别的现，最致命的是通过一般的本人相片也轻松进行误识别发生很大的安全隐患。
有鉴于此，如今迫切需要设计一种新的信息识别方法，以便克服现有识别方式存在的上述缺陷。
发明内容
本发明所要解决的技术问题是：提供一种利用汗腺位置信息的信息识别方法，可提高识别精度，有效降低误识别率。
此外，本发明还提供一种利用汗腺位置信息的信息识别系统，可提高识别精度，有效降低误识别率。
为解决上述技术问题，本发明采用如下技术方案：
一种利用汗腺位置信息的信息识别方法，所述识别方法包括：
步骤S1、从指纹图像中提取汗腺的位置信息并储存；
提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；
步骤S2、获取新录入的指纹图像；
步骤S3、对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺进行比较，判断是否为相同，通过汗腺位置信息进行比对；
在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
一种利用汗腺位置信息的信息识别方法，所述识别方法包括：
步骤S1、将汗腺的位置信息储存于一存储模块；
步骤S2、获取新录入的指纹图像，并提取汗腺信息；
步骤S3、对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
作为本发明的一种优选方案，步骤S1中，从指纹图像中提取汗腺的位置信息并储存；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息。
作为本发明的一种优选方案，步骤S1中，从指纹图像中提取汗腺的位置信息并储存；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息。
作为本发明的一种优选方案，步骤S3中，在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
作为本发明的一种优选方案，步骤S3中，在比对过程中，位于各自不同脊 线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
作为本发明的一种优选方案，步骤S3中，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人。
作为本发明的一种优选方案，步骤S3中，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
一种利用汗腺位置信息的信息识别系统，所述识别系统包括：
汗腺位置信息数据库，用以存储汗腺的位置信息；
存储模块，用以存储汗腺的位置信息；
汗腺信息提取模块，用以获取新录入的指纹图像，并提取汗腺信息；
判断模块，用以对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
作为本发明的一种优选方案，所述汗腺信息提取模块还用来从指纹图像中提取汗腺的位置信息，并将其储存至所述存储模块中；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；
所述判断模块在在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，判断模块在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，判断模块在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，判断模块在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
本发明的有益效果在于：本发明提出的利用汗腺位置信息的信息识别方法及系统，可提高识别精度，有效降低误识别率。
附图说明
图1为可确认汗腺的一般指纹图像。
图2为便于说明本发明而图示化的汗腺和指纹图像。
图3为汗腺位置信息表。
图4为通过储存的汗腺位置信息恢复的指纹类型。
图5为通过相同脊线中的汗腺信息提取角度信息方法的示意图。
[根据细则91更正 08.08.2016]　
具体实施方式
下面结合附图详细说明本发明的优选实施例。
实施例一
请参阅图1，本发明揭示了一种利用汗腺位置信息的信息识别方法，所述识别方法包括：
【步骤S1】从指纹图像中提取汗腺的位置信息并储存；
提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；
【步骤S2】获取新录入的指纹图像；
【步骤S3】对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺进行比较，判断是否为相同，通过汗腺位置信息进行比对；
在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人；
或者/并且，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此 为特征通过汗腺位置信息进行识别是否为本人。
以上介绍本发明利用汗腺位置信息的信息识别方法，本发明在揭示上述识别方法的同时，还揭示一种利用汗腺位置信息的信息识别系统；所述识别系统包括：存储模块、汗腺信息提取模块、判断模块。
存储模块用以存储汗腺的位置信息；汗腺信息提取模块用以获取新录入的指纹图像，并提取汗腺信息。
判断模块，用以对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
所述汗腺信息提取模块还用来从指纹图像中提取汗腺的位置信息，并将其储存至所述存储模块中；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息。
所述判断模块在在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，判断模块在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，判断模块在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，判断模块在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
图1是可确认汗腺的一般指纹图像，此图中可以看出指纹的汗腺组成一条脊线。脊线结束的点为终点，开叉的点为分叉点。
图2是为了说明本发明而图示化的提取汗腺位置信息的解释图。100表示指纹图像传感器的窗口，110和120各表示汗腺的位置信息的X轴值(110)和Y轴值(120)。R1,R2,R3是脊线,S1,S2,…S16是连续脊线上的汗腺,B1是位于分叉点的汗腺，E1是位于终点的汗腺。
S3的位置信息由传感器的原点(130)向X轴的变化值(x’)和Y轴的变化值 (y’)表示，这时传感器的原点(130)的X和Y值分别为(0,0),S3的位置信息就是(x’,y’)。
表1
Rk Sn Bm Ep
Rk1 Sn1 Bm1 Ep1
Rk2 Sn2 Bm1 EP2
表2
Figure PCTCN2016085664-appb-000001
表3
Figure PCTCN2016085664-appb-000002
B:Branch；E:End；R:Ridge；S:Sweat Grand。
表1至表3为汗腺位置信息表。即图2中提取的汗腺位置信息如表1至表3所示的表格方式被储存。一个脊线由一个汗腺位置信息表格中被保存。表格数由脊线数决定。脊线表示为R1～Rk,除了位于终点和分叉点的汗腺即在脊线上汗腺表示为S1～Sn,位于分叉点的汗腺表示为B1～Bm,位于终点的汗腺表示为E1～Ep。脊线信息Rk中k是序数，表示包含汗腺的总脊线数。Sn的n表示除了位于终点和分叉点的汗腺之外的总汗腺数。Bm的m表示位于分叉点的总汗腺 数,Ep的p表示位于终点的总汗腺数。所以指纹图像传感器中所提取的总汗腺数为n+m+p，即所有汗腺数之和。Sn,Bm,Ep的位置可提取为图2中各从原点(130)向传感器的X轴(110),Y轴(120)所增加的变化值。
图3是通过保存的汗腺信息恢复指纹图像的示意图。通过利用如图3所示保存的汗腺位置信息，把相同脊线的汗腺位置信息用直线连接就可得到图4所示的曲线图。所生成的曲线图可通过模式识别等方法用于用户认证。
图4所示，通过利用相同脊线上汗腺位置信息，提取角度信息的方法例示图。在相同脊线上的汗腺位置用直线连接后可提取540汗腺的夹角500和550汗腺的夹角510及传感器X轴和汗腺位置延伸方向间的邻角信息，相邻汗腺距离信息。因此保存的汗腺位置信息直接可以套用基于传统指纹特征点方法进行用户认证和识别。
图5表示图4的指纹旋转一定角度后的情况。即使指纹旋转一定角度后，图4中540汗腺的夹角500和图5的540汗腺的夹角500’维持相同值。图4中550汗腺的夹角510和图5的汗腺夹角510’也维持相同值。旋转后的图与原来的图相比因为两个汗腺信息保持相同角度值而轻松判断是否为相同图像。
实施例二
一种利用汗腺位置信息的信息识别方法，所述识别方法包括：
【步骤S1】将汗腺的位置信息储存于一存储模块。
从指纹图像中提取汗腺的位置信息并储存；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息。
或者，将汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息。
【步骤S2】获取新录入的指纹图像，并提取汗腺信息。
【步骤S3】对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人。
或者/并且，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
综上所述，本发明提出的利用汗腺位置信息的信息识别方法及系统，可提高识别精度，有效降低误识别率。
这里本发明的描述和应用是说明性的，并非想将本发明的范围限制在上述实施例中。这里所披露的实施例的变形和改变是可能的，对于那些本领域的普通技术人员来说实施例的替换和等效的各种部件是公知的。本领域技术人员应该清楚的是，在不脱离本发明的精神或本质特征的情况下，本发明可以以其它形式、结构、布置、比例，以及用其它组件、材料和部件来实现。在不脱离本发明范围和精神的情况下，可以对这里所披露的实施例进行其它变形和改变。

Claims (10)

  1. 一种利用汗腺位置信息的信息识别方法，其特征在于，所述识别方法包括：
    步骤S1、从指纹图像中提取汗腺的位置信息并储存；
    提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；
    步骤S2、获取新录入的指纹图像；
    步骤S3、对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺进行比较，判断是否为相同，通过汗腺位置信息进行比对；
    在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
  2. 一种利用汗腺位置信息的信息识别方法，其特征在于，所述识别方法包括：
    步骤S1、将汗腺的位置信息储存于一存储模块；
    步骤S2、获取新录入的指纹图像，并提取汗腺信息；
    步骤S3、对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
  3. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S1中，从指纹图像中提取汗腺的位置信息并储存；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息。
  4. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S1中，从指纹图像中提取汗腺的位置信息并储存；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息。
  5. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S3中，在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
  6. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S3中，在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人。
  7. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S3中，在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人。
  8. 根据权利要求2所述的利用汗腺位置信息的信息识别方法，其特征在于：
    步骤S3中，在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
  9. 一种利用汗腺位置信息的信息识别系统，其特征在于，所述识别系统包括：
    汗腺位置信息数据库，用以存储汗腺的位置信息；
    存储模块，用以存储汗腺的位置信息；
    汗腺信息提取模块，用以获取新录入的指纹图像，并提取汗腺信息；
    判断模块，用以对新录入的指纹图像中的汗腺信息与已储存的指纹汗腺位置信息进行比较，判断是否为相同。
  10. 根据权利要求9所述的利用汗腺位置信息的信息识别系统，其特征在于：
    所述汗腺信息提取模块还用来从指纹图像中提取汗腺的位置信息，并将其储存至所述存储模块中；提取汗腺位置信息并储存过程中，位于相同脊线的汗腺试做相同群来储存信息；汗腺的类型分为位于脊线终端的汗腺信息，位于分叉点的汗腺信息及位于连续的脊线的汗腺信息；通过以上三种基本类型进行分类储存汗腺信息；
    所述判断模块在在比对过程中，位于相同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，判断模块在比对过程中，位于各自不同脊线的汗腺位置信息进行比较；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，判断模块在比对过程中，通过相邻的汗腺位置信息提取汗腺间的夹角和邻角；以此为特征通过汗腺位置信息进行识别是否为本人；
    或者/并且，判断模块在比对过程中，位于相同脊线的汗腺进行连接生成图表；以此为特征通过汗腺位置信息进行识别是否为本人。
PCT/CN2016/085664 2015-12-22 2016-06-14 利用汗腺位置信息的信息识别方法及系统 WO2017107406A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510971524.X 2015-12-22
CN201510971524.XA CN105426877B (zh) 2015-12-22 2015-12-22 利用汗腺位置信息的信息识别方法及系统

Publications (1)

Publication Number Publication Date
WO2017107406A1 true WO2017107406A1 (zh) 2017-06-29

Family

ID=55505076

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/085664 WO2017107406A1 (zh) 2015-12-22 2016-06-14 利用汗腺位置信息的信息识别方法及系统

Country Status (2)

Country Link
CN (1) CN105426877B (zh)
WO (1) WO2017107406A1 (zh)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105426877B (zh) * 2015-12-22 2019-09-10 金虎林 利用汗腺位置信息的信息识别方法及系统
CN106250890B (zh) * 2016-09-23 2020-05-05 南昌欧菲生物识别技术有限公司 一种指纹识别方法及装置
CN106599782B (zh) * 2016-11-08 2020-06-09 金虎林 利用虹膜特征点位置信息的认证方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6411728B1 (en) * 1997-07-29 2002-06-25 Indivos Corporation Association of finger pores and macrofeatures for identification of individuals
US20040114784A1 (en) * 2002-11-12 2004-06-17 Fujitsu Limited Organism characteristic data acquiring apparatus, authentication apparatus, organism characteristic data acquiring method, organism characteristic data acquiring program and computer-readable recording medium on which the program is recorded
CN1842805A (zh) * 2003-08-29 2006-10-04 皇家飞利浦电子股份有限公司 生物统计识别设备
CN103124977A (zh) * 2010-07-13 2013-05-29 斯科特·麦克纳尔蒂 用于感测生物特征信息的系统、方法及装置
CN104508675A (zh) * 2012-05-30 2015-04-08 斯科特·麦克纳尔蒂 用于生物信息的电磁检测和分析的系统、方法及装置
CN105426877A (zh) * 2015-12-22 2016-03-23 金虎林 利用汗腺位置信息的信息识别方法及系统

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2450479A (en) * 2007-06-22 2008-12-31 Warwick Warp Ltd Fingerprint recognition including preprocessing an image by justification and segmentation before plotting ridge characteristics in feature space
CN103294987A (zh) * 2012-03-05 2013-09-11 天津华威智信科技发展有限公司 指纹匹配方法与实现方式

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6411728B1 (en) * 1997-07-29 2002-06-25 Indivos Corporation Association of finger pores and macrofeatures for identification of individuals
US20040114784A1 (en) * 2002-11-12 2004-06-17 Fujitsu Limited Organism characteristic data acquiring apparatus, authentication apparatus, organism characteristic data acquiring method, organism characteristic data acquiring program and computer-readable recording medium on which the program is recorded
CN1842805A (zh) * 2003-08-29 2006-10-04 皇家飞利浦电子股份有限公司 生物统计识别设备
CN103124977A (zh) * 2010-07-13 2013-05-29 斯科特·麦克纳尔蒂 用于感测生物特征信息的系统、方法及装置
CN104508675A (zh) * 2012-05-30 2015-04-08 斯科特·麦克纳尔蒂 用于生物信息的电磁检测和分析的系统、方法及装置
CN105426877A (zh) * 2015-12-22 2016-03-23 金虎林 利用汗腺位置信息的信息识别方法及系统

Also Published As

Publication number Publication date
CN105426877B (zh) 2019-09-10
CN105426877A (zh) 2016-03-23

Similar Documents

Publication Publication Date Title
CN108960211B (zh) 一种多目标人体姿态检测方法以及系统
US9519838B2 (en) Character recognition method
CN108021912B (zh) 一种指纹识别的方法和装置
WO2019071664A1 (zh) 结合深度信息的人脸识别方法、装置及存储介质
KR101632912B1 (ko) 지문 인식을 이용한 사용자 인증 방법
CN107958443B (zh) 一种基于脊线特征和tps形变模型的指纹图像拼接方法
JP6024141B2 (ja) 生体情報処理装置、生体情報処理方法、および生体情報処理プログラム
US8577094B2 (en) Image template masking
CN103679147A (zh) 手机型号的识别方法与装置
EP2887267A1 (en) Biometric authentication device and reference data verification method
CN108416342A (zh) 一种结合细节点和细线结构的指纹识别方法
JPH0471079A (ja) 画像の位置合わせ方法
WO2017107406A1 (zh) 利用汗腺位置信息的信息识别方法及系统
CN103793642B (zh) 移动互联网掌纹身份认证方法
CN112487867B (zh) 基于增强三角剖分的视觉约束指纹识别方法
US20160247012A1 (en) Method and a System for Matching Fingerprint Images Obtained From Different Fingerprint Image Capturing Devices
KR102151474B1 (ko) 스마트 단말기를 이용한 비접촉 지문인증 방법
CN101582114A (zh) 基于旋转角度的指纹识别方法
TWI804843B (zh) 指紋匹配方法、裝置、可讀儲存介質與電子設備
CN108664940A (zh) 一种部分指纹匹配方法及系统
JP6365117B2 (ja) 情報処理装置、画像判定方法、及びプログラム
CN114882539B (zh) 一种静脉图像roi提取方法及装置
CN111178203A (zh) 签名审核方法、装置、计算机设备和存储介质
CN115984765A (zh) 基于双流分块网络的行人重识别方法、电子设备和介质
CN109213889A (zh) 一种客户信息合并的方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16877219

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16877219

Country of ref document: EP

Kind code of ref document: A1