WO2017161636A1 - 一种基于指纹的终端支付方法及装置 - Google Patents

一种基于指纹的终端支付方法及装置 Download PDF

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WO2017161636A1
WO2017161636A1 PCT/CN2016/080613 CN2016080613W WO2017161636A1 WO 2017161636 A1 WO2017161636 A1 WO 2017161636A1 CN 2016080613 W CN2016080613 W CN 2016080613W WO 2017161636 A1 WO2017161636 A1 WO 2017161636A1
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fingerprint
feature point
point
module
feature points
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PCT/CN2016/080613
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English (en)
French (fr)
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何华
葛福臻
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宇龙计算机通信科技(深圳)有限公司
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Publication of WO2017161636A1 publication Critical patent/WO2017161636A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/38—Payment protocols; Details thereof
    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4014—Identity check for transactions
    • G06Q20/40145—Biometric identity checks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G06Q20/326—Payment applications installed on the mobile devices
    • 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
    • G06V40/1347—Preprocessing; Feature extraction
    • G06V40/1359—Extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop
    • 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

Definitions

  • the present invention relates to the field of communications technologies, and in particular, to a fingerprint-based terminal payment method and apparatus.
  • the server after the user passes the fingerprint identification, the server also needs to send the first random verification code to the mobile terminal, and the verification code received by the mobile terminal is defined as a second random verification code, and the server determines the second random verification received.
  • the drawback of this method is that the generation of the random verification code is independent of the fingerprint feature, and the mapping relationship between the fingerprint and the random code cannot be guaranteed. Different fingerprints may be generated by the server to generate the same random code, so that the random code has timeliness requirements. The requirements for the network and other conditions are higher, which makes the process of fingerprint payment more complicated. Therefore, how to make payment more safely is a technical problem that a person skilled in the art needs to solve.
  • An object of the present invention is to provide a fingerprint-based terminal payment method and apparatus, which can apply a location relationship of a fingerprint feature point to a payment function of a terminal based on the uniqueness of the fingerprint to improve the security of payment.
  • the present invention provides a fingerprint-based terminal payment method, including:
  • the processing results are arranged according to a predetermined rule to obtain a payment code.
  • the matching the fingerprint feature points with the pre-stored standard feature points and determining whether the matching is successful includes:
  • the calculating the distance value of each selected feature point to the center point of the fingerprint module includes:
  • the distance value of each selected feature point to the center point of the fingerprint module is calculated.
  • the processing of each distance value according to a predetermined normalization method, and obtaining the processing result comprising:
  • the ratio values of the respective distance values and the unit scale are respectively calculated, and the respective scale values are rounded and respectively used as the mapped values of the corresponding selected feature points.
  • the obtaining the processing result according to a predetermined rule to obtain a payment code includes:
  • mapping values Randomly arranging the mapping values to obtain a random code having the predetermined number of digits
  • the invention also provides a fingerprint-based terminal payment device, comprising:
  • An acquisition module configured to acquire fingerprint feature points of the collected fingerprint information
  • a feature point matching module configured to match the fingerprint feature point with a pre-stored standard feature point, and determine whether the matching is successful
  • a feature point selection module if yes, determining a predetermined number of selected feature points from the fingerprint feature points
  • a distance calculation module configured to calculate a distance value of each selected feature point to a reference point of the fingerprint module
  • a normalization module for processing each distance value according to a predetermined normalization method to obtain a processing result
  • a payment code obtaining module configured to arrange the processing result according to a predetermined rule to obtain a payment code.
  • the feature point matching module includes:
  • a reference point determining unit configured to compare the fingerprint feature point with a pre-stored standard feature point to determine a reference point
  • a calibration unit configured to calibrate the collected fingerprint image and the pre-stored fingerprint image by using the reference point
  • the determining unit is configured to determine whether the similarity is greater than a similarity threshold, and if yes, the matching is successful.
  • the distance calculation module includes:
  • a coordinate system establishing unit configured to use a center point of the fingerprint module as a coordinate origin to establish a Cartesian coordinate system
  • a coordinate determining unit configured to determine coordinates of each selected feature point according to the Cartesian coordinate system
  • the distance calculation unit is configured to calculate a distance value of each selected feature point to a center point of the fingerprint module by using coordinates of each selected feature point.
  • the normalization module includes:
  • a unit scale calculation unit for selecting a maximum distance value, and dividing the maximum distance value by a predetermined interval value to obtain a unit scale
  • the mapping value calculation unit is configured to separately calculate a proportional value of each distance value and the unit scale, and round each scale value as a mapped value of the corresponding selected feature point.
  • the payment code acquisition module includes:
  • a random code obtaining unit configured to randomly arrange the mapping values to obtain a random code having the predetermined number of digits
  • a payment code acquisition unit configured to convert the random code into a barcode or a two-dimensional code.
  • the fingerprint-based terminal payment method comprises: acquiring fingerprint feature points of the collected fingerprint information; matching the fingerprint feature points with pre-stored standard feature points, and determining whether the matching is successful; if yes, And determining a predetermined number of selected feature points from the fingerprint feature points; calculating a distance value of each selected feature point to a reference point of the fingerprint module; processing each distance value according to a predetermined normalization method, and obtaining a processing result; Arranging the processing results according to predetermined rules, obtaining branches Pay code
  • the method generates a unique payment code based on the positional relationship between the fingerprint feature points and applies it to the terminal payment function. Due to the uniqueness of the fingerprint, the positional relationship of the fingerprint feature points is also unique, and the obtained payment code and the collected fingerprint are obtained. There is a mapping relationship between the information, and applying it to the payment process can improve the security based on fingerprint payment and simplify the payment process.
  • the invention also discloses a fingerprint-based terminal payment device.
  • FIG. 1 is a flowchart of a fingerprint-based terminal payment method according to an embodiment of the present invention
  • FIG. 3 is a flowchart of calculating a distance value of each selected feature point to a reference point of a fingerprint module according to an embodiment of the present invention
  • FIG. 4 is a structural block diagram of a fingerprint-based terminal payment device according to an embodiment of the present invention.
  • the core of the present invention is to provide a fingerprint-based terminal payment method and device, which can apply the location relationship of fingerprint feature points to the payment function of the terminal based on the uniqueness of the fingerprint to improve the security of payment.
  • FIG. 1 is a flowchart of a fingerprint-based terminal payment method according to an embodiment of the present invention; the method may include:
  • the step may include fingerprint collection, fingerprint preprocessing, and fingerprint feature point extraction.
  • fingerprint acquisition technologies include optical total reflection technology, silicon crystal sensor technology, and ultrasonic scanning technology; wherein, fingerprints on mobile terminals such as mobile phones are usually It is collected by sensors.
  • the fingerprint preprocessing process consists of five parts: normalization, segmentation, filter enhancement, binarization, and refinement.
  • the purpose of normalizing the fingerprint image is to enhance the contrast of the ridge line and the valley line, so that the fingerprint image is at the same brightness level, which facilitates the threshold selection of the subsequent segmentation process.
  • the fingerprint image I is divided into N ⁇ N blocks, and I(i,j) represents image gradation values in the iith row and the jth column. Assuming the image resolution is 500dpi, the average gray value M(I) and variance VAR(I) of the fingerprint image are:
  • M 0 is the desired gray level average and VAR 0 is the desired variance value.
  • the grayscale values of the normalized fingerprint images are all near M 0 .
  • the fingerprint image is segmented into the foreground and the background, so that the subsequent operation is performed only on the foreground region, thereby improving the processing efficiency; then, the noise is further removed, and the ridge break is connected, and a Gabor filter or a direction template filter may be used;
  • the binarization operation is performed, that is, the ridge line extraction operation.
  • the texture of the ridge line and the valley line becomes very clear, and the direction division method is a method of binarization using direction information, and the method also has A certain enhancement effect; finally extracting the skeleton information of the ridge line, removing redundant information unrelated to feature extraction, and reducing the storage amount of information.
  • the fingerprint features include global features and local features.
  • the former is suitable for fingerprint classification, and the latter is used for fingerprint matching. Therefore, in the present invention, only local features need to be extracted.
  • the local feature is the detail in the fingerprint, which can be obtained by the refined fingerprint image, because the gray value of all the pixels in the refined fingerprint image can only be 0 or 1, and 0 represents the background point gray scale (white ), 1 indicates the gradation gray scale (black).
  • the most common detail feature is the tip and bifurcation points proposed by the National Institute of Standards (NIST).
  • the point to be detected P(i, j) is taken as the center point, as shown in the following figure, according to the relationship between the gray values of adjacent points, It can be determined whether the point to be detected is a feature point.
  • I(K) is the gray value corresponding to each point.
  • Pseudo-feature points are included in the extraction process of feature points, and it is necessary to remove them.
  • the segmentation process in the pre-processing will cause the edge portion of the foreground region to be displayed as many endpoints, which should be removed first.
  • the positional relationship between the feature points can be used as the basis for filtering the pseudo-feature points.
  • fingerprint matching judgment is needed: firstly, the extracted fingerprint detail feature point set is compared with the preset fingerprint feature point set stored in the terminal, that is, the pre-stored standard feature points are compared and found.
  • the reference point is used to calibrate the two fingerprint images; then the similarity of the two fingerprint detail feature point sets is obtained; finally, the similarity threshold is set based on the actual demand, and if the obtained similarity is higher than the threshold, the matching is successful. Conversely, the match failed.
  • Figure 2 where the process may specifically include:
  • S210 calibrate the collected fingerprint image and the pre-stored fingerprint image by using the reference point
  • S220 Calculate a similarity between the calibrated fingerprint feature point and a pre-stored standard feature point
  • the security of the payment can be improved by the above matching process, and it is confirmed that the payment operation is initiated by the user.
  • the method not only uses the uniqueness of the fingerprint to perform user identity confirmation, but also utilizes the uniqueness of the positional relationship of the detailed feature points of the fingerprint.
  • the mapping relationship between the selected feature points and the fingerprint image is formed to form a payment code to simplify the payment process.
  • the number of selected feature points is determined from the fingerprint feature points according to actual conditions. For example, the requirements for high security of payment are not high, and some feature points may be selected as selected feature points; The performance requirement is lower than the speed requirement, and some feature points may be selected as the selected feature points; if the payment security level requirement is high and the speed requirement is high, some feature points may be selected as the selected feature points, and the device is enhanced. Hardware configuration. Usually, 16 feature points can be selected as the selected feature points.
  • the method is to use the uniqueness of the positional relationship of the fingerprint feature points.
  • a reference point is needed to calculate the relative distance value of each selected feature point. It is usually convenient to calculate the coordinate system by reference point.
  • the center point of the fingerprint module may be selected as a reference point.
  • the distance value of each selected feature point to the reference point of the fingerprint module which may specifically include:
  • S320 Calculate a distance value of each selected feature point to a center point of the fingerprint module by using coordinates of each selected feature point.
  • each selected feature point when calculating the distance value of each selected feature point to the center point of the fingerprint module, each selected feature point may be first sorted and identified by a numerical value to distinguish each selected feature point, the sorting
  • the values from 0 to N can be sequentially named according to the order in which the feature points are extracted.
  • the step is normalized, so that the distance value of the absolute value becomes a certain relative value relationship, which can simplify the calculation and reduce the magnitude.
  • the specific normalization method can be selected according to the situation, and it is generally subject to unitization. Limit all distance values to a fixed value range.
  • the processing according to the predetermined normalization method, the value of each distance is obtained, and the processing result is obtained, which may include:
  • the predetermined interval value may be determined according to a specific situation, and the general value is selected to be a value of 0; therefore, the predetermined interval value herein may be 9.
  • the ratio values of the respective distance values and the unit scale are respectively calculated, and the respective scale values are rounded and respectively used as the mapped values of the corresponding selected feature points.
  • each selected feature point corresponds to an integer within a predetermined interval value, for example, a value between 0 and 9.
  • the normalized values corresponding to the selected feature points are sorted, and the payment code can be formed according to the sorted numerical code.
  • the ordering here may be performed randomly, or may be sorted from small to large or from large to small according to the serial number values of each selected feature point when the selected feature points are previously selected.
  • the specific collation is not limited here, and only a set of numeric codes can be generated based on the normalized mapping values.
  • the payment code here may be a set of numerical random codes generated by the normalized mapping values, or may be a barcode generated by generating a set of numerical random codes according to the normalized mapping values, or may be based on normalization.
  • the latter mapping value generates a two-dimensional code generated after a set of numerical random codes.
  • the process is performed according to a predetermined rule, and the obtaining the payment code may include:
  • mapping values Randomly arranging the mapping values to obtain a random code having the predetermined number of digits
  • the barcode can be scanned according to the barcode or the two-dimensional code generated by the device, which is convenient and quick, and has high safety factor.
  • d max MAX ⁇ d 1 ,d 2 ,...,d 16 ⁇
  • N i [n i ]
  • the scanning device on the payment side scans it to complete the payment process.
  • the fingerprint-based terminal payment method generateds a unique payment code based on a positional relationship between fingerprint feature points and is applied to a terminal payment function. Due to the uniqueness of the fingerprint, the fingerprint feature point is The positional relationship is also unique. The feature points of the fingerprint are extracted, and some feature points are randomly selected. The center of the fingerprint module is used as a reference point to determine the distance between each feature point and the reference point, thereby generating a payment code and using it. For the payment function. There is a mapping relationship between the thus obtained payment code and the collected fingerprint information, and the application to the payment process can improve the security based on the fingerprint payment and simplify the payment process.
  • the embodiment of the invention provides a fingerprint-based terminal payment method, which can apply the location relationship of the fingerprint feature points to the payment function of the terminal based on the uniqueness of the fingerprint, so as to improve the security of payment.
  • the fingerprint-based terminal payment device provided by the embodiment of the present invention is described below.
  • the fingerprint-based terminal payment device described below and the fingerprint-based terminal payment method described above may be mutually referenced.
  • FIG. 4 is a diagram of a fingerprint-based terminal payment device according to an embodiment of the present invention.
  • the device can include:
  • the acquiring module 100 is configured to acquire fingerprint feature points of the collected fingerprint information.
  • the feature point matching module 200 is configured to match the fingerprint feature point with a pre-stored standard feature point, and determine whether the matching is successful;
  • the feature point selection module 300 is configured to: if yes, determine a predetermined number of selected feature points from the fingerprint feature points;
  • the distance calculation module 400 is configured to calculate a distance value of each selected feature point to a center point of the fingerprint module
  • the normalization module 500 is configured to process each distance value according to a predetermined normalization method to obtain a processing result
  • the payment code obtaining module 600 is configured to arrange the processing result according to a predetermined rule to obtain a payment code.
  • the feature point matching module 300 may include:
  • a reference point determining unit configured to compare the fingerprint feature point with a pre-stored standard feature point to determine a reference point
  • a calibration unit configured to calibrate the collected fingerprint image and the pre-stored fingerprint image by using the reference point
  • a similarity calculation unit configured to calculate a similarity between the calibrated fingerprint feature point and a pre-stored standard feature point
  • the determining unit is configured to determine whether the similarity is greater than a similarity threshold, and if yes, the matching is successful.
  • the distance calculation module 400 may include:
  • a coordinate system establishing unit configured to use a center point of the fingerprint module as a coordinate origin to establish a Cartesian coordinate system
  • a coordinate determining unit configured to determine coordinates of each selected feature point according to the Cartesian coordinate system
  • the distance calculation unit is configured to calculate a distance value of each selected feature point to a center point of the fingerprint module by using coordinates of each selected feature point.
  • the normalization module 500 can include:
  • a unit scale calculation unit for selecting a maximum distance value, and dividing the maximum distance value by a predetermined interval value to obtain a unit scale
  • a map value calculation unit for respectively calculating a ratio of each distance value to the unit scale The value is rounded up and used as the mapped value of the corresponding selected feature point.
  • the payment code obtaining module 600 may include:
  • a random code obtaining unit configured to randomly arrange the mapping values to obtain a random code having the predetermined number of digits
  • a payment code acquisition unit configured to convert the random code into a barcode or a two-dimensional code.
  • the fingerprint-based terminal payment device may be a mobile terminal such as a mobile phone, a tablet, or the like.
  • the fingerprint-based terminal payment system provided by the embodiment of the present invention generates a unique random code and a barcode based on the positional relationship between the fingerprint detail feature points and applies to the terminal payment function. Due to the uniqueness of the fingerprint, the positional relationship of the detailed feature points of the fingerprint is also unique, and thus the mapping relationship between the obtained barcode and the fingerprint image is applied to the payment process, which can improve the security of the fingerprint payment and simplify the payment. process.
  • the steps of a method or algorithm described in connection with the embodiments disclosed herein can be implemented directly in hardware, a software module executed by a processor, or a combination of both.
  • the software module can be placed in random access memory (RAM), memory, read only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or technical field. Any other form of storage medium known.

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Abstract

本发明公开了一种基于指纹的终端支付方法，该方法包括：获取采集到的指纹信息的指纹特征点；将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；若是，则从所述指纹特征点中确定预定个数的选定特征点；计算各个选定特征点到指纹模组的参考点的距离数值；按照预定归一化方法处理各个距离数值，得到处理结果；将所述处理结果按照预定规则排列，获取支付码；该方法基于指纹的唯一性，将指纹特征点的位置关系应用到终端的支付功能，以提高支付的安全性；且不需要用户输入随机码等繁琐的操作，用户仅需要进行采集指纹操作即可；本发明还公开了一种基于指纹的终端支付装置。

Description

一种基于指纹的终端支付方法及装置
本申请要求于2016年3月25日提交中国专利局，申请号为201610177974.6、发明名称为“一种基于指纹的终端支付方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及通信技术领域，特别涉及一种基于指纹的终端支付方法及装置。
背景技术
现有技术中，在用户进行指纹识别通过后，服务器还需要发送第一随机验证码至移动终端，移动终端接收到的验证码定义为第二随机验证码，服务器判断接收到的第二随机验证码的时效性，并最终判断两个随机验证码的一致性来完成支付。
此种方法的缺陷在于随机验证码的生成和指纹特征无关，不能保证指纹和随机码之间的映射关系，不同的指纹被服务器识别后可能会生成相同的随机码，使得随机码具有时效性要求，对网络等条件要求较高，从而致使指纹支付的过程更为繁琐。因此，如何更加安全的进行支付，是本领域技术人员需要解决的技术问题。
发明内容
本发明的目的是提供一种基于指纹的终端支付方法及装置，能够基于指纹的唯一性，将指纹特征点的位置关系应用到终端的支付功能，以提高支付的安全性。
为解决上述技术问题，本发明提供一种基于指纹的终端支付方法，包括：
获取采集到的指纹信息的指纹特征点；
将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
若是，则从所述指纹特征点中确定预定个数的选定特征点；
计算各个选定特征点到指纹模组的参考点的距离数值；
按照预定归一化方法处理各个距离数值，得到处理结果；
将所述处理结果按照预定规则排列，获取支付码。
其中，所述将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功，包括：
将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
计算校准后的所述指纹特征点与预存的标准特征点的相似度；
判断所述相似度是否大于相似度阈值；
若是，则匹配成功。
其中，所述计算各个选定特征点到指纹模组的中心点的距离数值，包括：
将指纹模组的中心点作为坐标原点，建立直角坐标系；
根据所述直角坐标系，确定各个选定特征点的坐标；
利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
其中，所述按照预定归一化方法处理各个距离数值，得到处理结果，包括：
选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
分别计算各个距离数值与所述单位刻度的比例数值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
其中，所述将所述处理结果按照预定规则排列，获取支付码，包括：
将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
将所述随机码转化成条形码或二维码。
本发明还提供一种基于指纹的终端支付装置，包括：
采集模块，用于获取采集到的指纹信息的指纹特征点；
特征点匹配模块，用于将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
特征点选取模块，用于若是，则从所述指纹特征点中确定预定个数的选定特征点；
距离计算模块，用于计算各个选定特征点到指纹模组的参考点的距离数值；
归一化模块，用于按照预定归一化方法处理各个距离数值，得到处理结果；
支付码获取模块，用于将所述处理结果按照预定规则排列，获取支付码。
其中，所述特征点匹配模块包括：
参考点确定单元，用于将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
校准单元，用于利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
相似度计算单元，用于计算校准后的所述指纹特征点与预存的标准特征点的相似度；
判断单元，用于判断所述相似度是否大于相似度阈值，若是，则匹配成功。
其中，所述距离计算模块包括：
坐标系建立单元，用于将指纹模组的中心点作为坐标原点，建立直角坐标系；
坐标确定单元，用于根据所述直角坐标系，确定各个选定特征点的坐标；
距离计算单元，用于利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
其中，所述归一化模块包括：
单位刻度计算单元，用于选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
映射值计算单元，用于分别计算各个距离数值与所述单位刻度的比例数值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
其中，所述支付码获取模块包括：
随机码获取单元，用于将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
支付码获取单元，用于将所述随机码转化成条形码或二维码。
本发明所提供的基于指纹的终端支付方法，该方法包括：获取采集到的指纹信息的指纹特征点；将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；若是，则从所述指纹特征点中确定预定个数的选定特征点；计算各个选定特征点到指纹模组的参考点的距离数值；按照预定归一化方法处理各个距离数值，得到处理结果；将所述处理结果按照预定规则排列，获取支 付码；
该方法基于指纹特征点之间的位置关系生成唯一支付码并应用于终端支付功能，由于指纹的唯一性，指纹特征点的位置关系同样具有唯一性，由此得到的支付码和采集到的指纹信息间存在映射关系，将其应用于支付过程可以提高基于指纹支付的安全性，并且简化了支付过程。本发明还公开了一种基于指纹的终端支付装置。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1为本发明实施例所提供的基于指纹的终端支付方法的流程图；
图2为本发明实施例所提供的指纹特征点进行匹配的流程图；
图3为本发明实施例所提供的计算各个选定特征点到指纹模组的参考点的距离数值的流程图；
图4为本发明实施例所提供的基于指纹的终端支付装置的结构框图。
具体实施方式
本发明的核心是提供一种基于指纹的终端支付方法及装置，能够基于指纹的唯一性，将指纹特征点的位置关系应用到终端的支付功能，以提高支付的安全性。
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
请参考图1，图1为本发明实施例所提供的基于指纹的终端支付方法的流程图；该方法可以包括：
S100、获取采集到的指纹信息的指纹特征点；
其中，该步骤可以包括指纹采集、指纹预处理、指纹特征点提取；其中，常用的指纹采集技术有光学全反射技术、硅晶体传感器技术和超声波扫描技术；其中，移动终端例如手机上的指纹通常是通过传感器采集的。指纹预处理过程包含五个部分：归一化、分割、滤波增强、二值化和细化。
对指纹图像进行归一化操作的目的是增强脊线和谷线的对比度，使指纹图像处在同一亮度级上，便于后续的分割处理的阈值选取。将指纹图像I分成N×N块，I(i,j)表示在第ii行、第j列的图像灰度值。假设图像分辨率是500dpi，那么指纹图像的平均灰度值M(I)和方差VAR(I)分别为：
Figure PCTCN2016080613-appb-000001
Figure PCTCN2016080613-appb-000002
对M(I)和VAR(I)进行归一化处理，归一化的公式为：
Figure PCTCN2016080613-appb-000003
式中M0是期望的灰度平均值，VAR0是期望的方差值。归一化后的指纹图像的灰度值都在M0附近。
接着对指纹图像进行前景和背景的分割，使后续操作只对前景区进行从而可以提高处理效率；然后需要进一步去除噪声，连接脊线断裂的地方，可以使用Gabor滤波器或是方向模板滤波器；滤波之后再进行二值化操作，也即脊线提取操作，二值化之后脊线和谷线的纹路变得十分清晰，方向分割法是利用方向信息进行二值化的方法，此法还具有一定的增强效果；最后提取脊线的骨架信息，去除与特征提取无关的冗余信息，减少信息的存储量。
指纹特征包括全局特征和局部特征，前者适用于指纹的分类，后者用于指纹的匹配，所以本发明中只需要提取局部特征。局部特征是指纹中的细节，可以通过细化后的指纹图像求的，因为细化后的指纹图像中的所有像素点的灰度值只能为0或者1，0表示背景点灰度(白色)，1表示纹线点灰度(黑色)。 最常用的细节特征是美国国家标准局(NIST)提出的末梢点和分叉点。
获得细化后的指纹图像后，在一个3×3的模板中，以待检测点P(i,j)为中心点，如下图所示，根据相邻各点灰度值之间的关系，可以判断待检测点是否为特征点。
P4(i+1,j-1) P3(i+1,j) P2(i+1,j+1)
P5(i,j-1) P(i,j) P1(i,j+1)
P6(i-1,j-1) P7(i-1,j) P8(i-1,j+1)
如果相邻各点灰度值满足下式时，则P为末梢点。
Figure PCTCN2016080613-appb-000004
如果相邻各点灰度值满足下式时，则P为分叉点。
Figure PCTCN2016080613-appb-000005
式中，I(K)为各点对应的灰度值。
对细化后的指纹图像，沿着脊线进行从上到下、从左至右的遍历，以上述两式为判断依据，保存特征点的类型和位置，并获取局部脊线的方向和采样点信息。
特征点的提取过程中会包含伪特征点，有必要将其去除。预处理中的分割过程会使得前景区的边缘部分显示为很多端点，应当首先去除；另外，特征点间的位置关系可以作为滤除伪特征点的依据。
S110、将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
其中，为了达到安全支付的目的，特征点提取完成后需要进行指纹匹配判断：首先将提取的指纹细节特征点集合与终端中保存的指纹细节特征点集合即预存的标准特征点进行比对，找到参考点，对两幅指纹图像进行校准；然后求得两个指纹细节特征点集合的相似度；最后，基于实际需求，设置相似度阈值，如果求得的相似度高于此阈值，则匹配成功，反之，匹配失败。具体过程可以参照图2，该过程具体可以包括：
S200、将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
S210、利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
S220、计算校准后的所述指纹特征点与预存的标准特征点的相似度；
S230、判断所述相似度是否大于相似度阈值，若是，则匹配成功。
其中，通过上述匹配过程可以提高支付的安全性，确认该支付操作是由用户发起。
S120、若是，则从所述指纹特征点中确定预定个数的选定特征点；
其中，为了进一步提高利用指纹支付的安全性，该方法不仅要用到指纹的唯一性来进行用户身份确认，还利用指纹的细节特征点的位置关系的唯一性。得到选取的特征点与指纹图像之间的映射关系形成支付码，来简化支付的过程。
这里从指纹特征点中确定选定特征点的个数根据实际情况进行确定，例如对支付安全级性要求高对速度要求不高，可以多选取一些特征点作为选定特征点；对支付安全级性要求低于对速度要求，可以少选取一些特征点作为选定特征点；若对支付安全级性要求高对速度要求也高，可以多选取一些特征点作为选定特征点，且增强该装置的硬件配置。通常情况下可以选择16个特征点作为选定特征点即可。
S130、计算各个选定特征点到指纹模组的参考点的距离数值；
其中，该方法要利用指纹特征点的位置关系的唯一性，若要利用选取的断定特征点的位置关系，需要一个参考点来计算各个选定特征点的相对距离数值。通常以参考点选取坐标系会方便计算。
优选的，为了方便计算，可以选取指纹模组的中心点作为参考点。请参考图3即计算各个选定特征点到指纹模组的参考点的距离数值具体可以包括：
S300、将指纹模组的中心点作为坐标原点，建立直角坐标系；
S310、根据所述直角坐标系，确定各个选定特征点的坐标；
S320、利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
其中，可选的这里在计算各个选定特征点到指纹模组的中心点的距离数值时，可以首先将各个选定特征点进行排序并以数值进行标识以便区分各个选定特征点，该排序可以根据抽取特征点的顺序依次用从0到N的数值进行命名。
S140、按照预定归一化方法处理各个距离数值，得到处理结果；
其中，该步骤进行归一化处理，可以使得绝对值的距离数值变成某种相对值关系，能够简化计算，缩小量值。具体的归一化方法可以根据情况进行选择，一般都是要经过单位化处理。将所有的距离数值都限定到一个固定的数值区间之内。
可选的，所述按照预定归一化方法处理各个距离数值，得到处理结果，具体可以包括：
选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
其中，预定区间数值可以根据具体情况进行确定，一般数值都选取0值9；因此，这里的预定区间数值可以是9。
分别计算各个距离数值与所述单位刻度的比例数值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
其中，这里的取整可以方便形成支付码的计算，这样每一位选定特征点对应的都是一个预定区间数值之内的整数，例如0至9之间的数值。
S150、将所述处理结果按照预定规则排列，获取支付码。
其中，这里将各个选定特征点对应的归一化处理后的值进行排序，根据排序后的数值码可以形成支付码。
这里的排序可以是随机进行，也可以是按照之前选取选定特征点时每个选定特征点的序号数值从小到大排序或者从大到小排序。这里并不对具体的排序规则进行限定，只需要能够依据归一化后的映射值产生一组数值代码即可。
这里的支付码可以是由归一化后的映射值产生一组数值随机码，也可以是根据归一化后的映射值产生一组数值随机码后生成的条形码，也可以是根据归一化后的映射值产生一组数值随机码后生成的二维码等。
可选的，所述将所述处理结果按照预定规则排列，获取支付码具体可以包括：
将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
将所述随机码转化成条形码或二维码。
这样用户在进行支付时，只需要在装置上录入指纹信息，就可以根据该装置生成的条形码或二维码进行扫码支付，方便快捷，且安全系数高。
下面以16个选定特征点为例说明上述S130到S150的过程：
以指纹模组的中心为坐标原点、1mm为刻度建立直角坐标系，确定16个特征点的坐标Ni(xi,yi)，i＝1,2,……16。计算各点到原点的距离di(i＝1,2,……16)，并求得距离的最大值dmax。
Figure PCTCN2016080613-appb-000006
dmax＝MAX{d1,d2,…,d16}
将dmax除以9，得到di映射到数字0-9的单位刻度U，即
U＝dmax/9
然后求得di(i＝1,2,……16)与U之间的比例关系，
ni＝di/U
对ni进行取整，从而将di(i＝1,2,……16)映射到数字0-9。
Ni＝[ni]
将ni(i＝1,2,……16)按照i的升序进行排列，获得一个16位的随机码，并将其转换成条形码，显示在移动终端上。支付端的扫描设备对其进行扫描，完成支付过程。
基于上述技术方案，本发明实施例提供的基于指纹的终端支付方法，该方法基于指纹特征点之间的位置关系生成唯一支付码并应用于终端支付功能，由于指纹的唯一性，指纹特征点的位置关系同样具有唯一性，提取指纹的特征点，并随机选取部分特征点，以指纹模组的中心为参考点，确定各个特征点到参考点的距离，以此生成支付码，并将其用于支付功能。由此得到的支付码和采集到的指纹信息间存在映射关系，且应用于支付过程可以提高基于指纹支付的安全性，并且简化了支付过程。
本发明实施例提供了基于指纹的终端支付方法，能够基于指纹的唯一性，将指纹特征点的位置关系应用到终端的支付功能，以提高支付的安全性。
下面对本发明实施例提供的基于指纹的终端支付装置进行介绍，下文描述的基于指纹的终端支付装置与上文描述的基于指纹的终端支付方法可相互对应参照。
请参考图4，图4为本发明实施例所提供的基于指纹的终端支付装置的结 构框图，该装置可以包括：
采集模块100，用于获取采集到的指纹信息的指纹特征点；
特征点匹配模块200，用于将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
特征点选取模块300，用于若是，则从所述指纹特征点中确定预定个数的选定特征点；
距离计算模块400，用于计算各个选定特征点到指纹模组的中心点的距离数值；
归一化模块500，用于按照预定归一化方法处理各个距离数值，得到处理结果；
支付码获取模块600，用于将所述处理结果按照预定规则排列，获取支付码。
可选的，所述特征点匹配模块300可以包括：
参考点确定单元，用于将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
校准单元，用于利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
相似度计算单元，用于计算校准后的所述指纹特征点与预存的标准特征点的相似度；
判断单元，用于判断所述相似度是否大于相似度阈值，若是，则匹配成功。
可选的，所述距离计算模块400可以包括：
坐标系建立单元，用于将指纹模组的中心点作为坐标原点，建立直角坐标系；
坐标确定单元，用于根据所述直角坐标系，确定各个选定特征点的坐标；
距离计算单元，用于利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
可选的，所述归一化模块500可以包括：
单位刻度计算单元，用于选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
映射值计算单元，用于分别计算各个距离数值与所述单位刻度的比例数 值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
可选的，所述支付码获取模块600可以包括：
随机码获取单元，用于将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
支付码获取单元，用于将所述随机码转化成条形码或二维码。
其中，该基于指纹的终端支付装置可以是移动终端如手机，平板等。
基于上述技术方案，本发明实施例提供的基于指纹的终端支付系统，基于指纹细节特征点之间的位置关系生成唯一随机码和条形码并应用于终端支付功能的装置。由于指纹的唯一性，指纹的细节特征点的位置关系同样具有唯一性，由此得到的条形码和指纹图像间存在映射关系，将其应用于支付过程可以提高指纹支付的安全性，并且简化了支付过程。
说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
专业人员还可以进一步意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处理器执行的软件模块，或者二者的结合来实施。软件模块可以置于随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、硬盘、可移动磁盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质中。
以上对本发明所提供的基于指纹的终端支付方法及系统进行了详细介绍。本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想。应当指出，对于本技术领 域的普通技术人员来说，在不脱离本发明原理的前提下，还可以对本发明进行若干改进和修饰，这些改进和修饰也落入本发明权利要求的保护范围内。

Claims (10)

  1. 一种基于指纹的终端支付方法，其特征在于，包括：
    获取采集到的指纹信息的指纹特征点；
    将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
    若是，则从所述指纹特征点中确定预定个数的选定特征点；
    计算各个选定特征点到指纹模组的参考点的距离数值；
    按照预定归一化方法处理各个距离数值，得到处理结果；
    将所述处理结果按照预定规则排列，获取支付码。
  2. 如权利要求1所述的基于指纹的终端支付方法，其特征在于，所述将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功，包括：
    将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
    利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
    计算校准后的所述指纹特征点与预存的标准特征点的相似度；
    判断所述相似度是否大于相似度阈值；
    若是，则匹配成功。
  3. 如权利要求1所述的基于指纹的终端支付方法，其特征在于，所述计算各个选定特征点到指纹模组的参考点的距离数值，包括：
    将指纹模组的中心点作为坐标原点，建立直角坐标系；
    根据所述直角坐标系，确定各个选定特征点的坐标；
    利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
  4. 如权利要求3所述的基于指纹的终端支付方法，其特征在于，所述按照预定归一化方法处理各个距离数值，得到处理结果，包括：
    选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
    分别计算各个距离数值与所述单位刻度的比例数值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
  5. 如权利要求4所述的基于指纹的终端支付方法，其特征在于，所述将所述处理结果按照预定规则排列，获取支付码，包括：
    将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
    将所述随机码转化成条形码或二维码。
  6. 一种基于指纹的终端支付装置，其特征在于，包括：
    采集模块，用于获取采集到的指纹信息的指纹特征点；
    特征点匹配模块，用于将所述指纹特征点与预存的标准特征点进行匹配，并判断匹配是否成功；
    特征点选取模块，用于若是，则从所述指纹特征点中确定预定个数的选定特征点；
    距离计算模块，用于计算各个选定特征点到指纹模组的参考点的距离数值；
    归一化模块，用于按照预定归一化方法处理各个距离数值，得到处理结果；
    支付码获取模块，用于将所述处理结果按照预定规则排列，获取支付码。
  7. 如权利要求6所述的基于指纹的终端支付装置，其特征在于，所述特征点匹配模块包括：
    参考点确定单元，用于将所述指纹特征点与预存的标准特征点进行对比，确定参考点；
    校准单元，用于利用所述参考点对采集的指纹图像及预存的指纹图像进行校准；
    相似度计算单元，用于计算校准后的所述指纹特征点与预存的标准特征点的相似度；
    判断单元，用于判断所述相似度是否大于相似度阈值，若是，则匹配成功。
  8. 如权利要求6所述的基于指纹的终端支付装置，其特征在于，所述距离计算模块包括：
    坐标系建立单元，用于将指纹模组的中心点作为坐标原点，建立直角坐标系；
    坐标确定单元，用于根据所述直角坐标系，确定各个选定特征点的坐标；
    距离计算单元，用于利用各个选定特征点的坐标，计算各个选定特征点到指纹模组的中心点的距离数值。
  9. 如权利要求8所述的基于指纹的终端支付装置，其特征在于，所述归一化模块包括：
    单位刻度计算单元，用于选定最大的距离数值，并将所述最大的距离数值除以预定区间数值，得到单位刻度；
    映射值计算单元，用于分别计算各个距离数值与所述单位刻度的比例数值，并将各个比例数值取整后分别作为对应的选定特征点的映射值。
  10. 如权利要求9所述的基于指纹的终端支付装置，其特征在于，所述支付码获取模块包括：
    随机码获取单元，用于将所述映射值随机排列，获取具有所述预定个数的数字的随机码；
    支付码获取单元，用于将所述随机码转化成条形码或二维码。
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