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Information technology -- Biometrics -- Multimodal and other multibiometric fusion
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GB/T 36460-2018
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Basic data | Standard ID | GB/T 36460-2018 (GB/T36460-2018) | | Description (Translated English) | Information technology -- Biometrics -- Multimodal and other multibiometric fusion | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L71 | | Classification of International Standard | 35.240.15 | | Word Count Estimation | 22,276 | | Date of Issue | 2018-06-07 | | Date of Implementation | 2019-01-01 | | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration |
GB/T 36460-2018: Information technology -- Biometrics -- Multimodal and other multibiometric fusion ---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
Information technology--Biometrics--Multimodal and other multibiometric fusion
ICS 35.240.15
L71
National Standards of People's Republic of China
Information Technology Biometrics
Multi-modality and other multi-biological fusion
(ISO /IEC TR24722.2015, MOD)
Published on.2018-06-07
2019-01-01 Implementation
National Market Supervision Administration
China National Standardization Administration released
Directory
Preface I
Introduction II
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
4 Multi-modal and other multiple biometric identification systems 2
5 Combination Level 5
6 Characterization data for multiple biometric identification systems 15
Reference 16
Foreword
This standard was drafted in accordance with the rules given in GB/T 1.1-2009.
This standard uses the redrafted law to amend the use of ISO /IEC TR24722.2015 "Multiple modalities of biometrics in information technology and
Other multiple biometric fusions. Compared with ISO /IEC TR24722.2015, this standard has made certain adjustments in the structure, adding a second
Chapters normative references, follow-up chapter number is also adjusted accordingly; at the same time in ISO /IEC TR24722.2015 3.1 added
(See 4.1).
The technical differences between this standard and ISO /IEC TR24722.2015 and its causes are as follows.
--- Modify the description of the scope of application of the standard (see Chapter 1), according to national standards to increase the "prescribed" and "applicable to"
Content, etc.
--- With regard to normative references, this standard has made adjustments that are technically different. The adjustments are centrally reflected in Chapter 2, "Regulations."
In the "Citizen References", the specific adjustments are as follows.
● Added reference to ISO /IEC 2382-37;
● Added reference to ISO /IEC 29159-1;
--- Deleted the original 3.1 description of the current research work (see 4.1) to make the content clearer;
--- Revised the original 3.1 multi biometric classification statement (see 4.1) to make the content clearer;
--- Modify the description of the original 4.3.2 section, add a description of the characteristics (see 5.3.2);
--- Modify the description of the original 4.4 on feature-level fusion practice (see 5.4), so that the content is more clear;
--- Deleted the descriptive description of the original 4.4 (see 5.4) to make the content clearer;
--- Modify the description of the original 5.1 (see 6.1), so that the content is more clear.
This standard also made the following editorial changes.
---4.4 The description of the reasons for the difficulty of feature-level fusion practice is described as "notes" (see 5.4).
Please note that some of the contents of this document may involve patents. The issuing agency of this document does not assume responsibility for identifying these patents.
This standard was proposed and managed by the National Information Technology Standardization Technical Committee (SAC/TC28).
This standard was drafted by. China Electronics Standardization Institute, Guangzhou Guangdian Express Financial Electronics Co., Ltd., Guangzhou Guangdian Zhuo
Knowledge Intelligence Technology Co., Ltd., Beijing Tiancheng Shengye Technology Co., Ltd., Shanxi Tiandi Technology Co., Ltd., Human Resources and Social Security Department
Center, Changchun Hongda Optoelectronics and Biometric Identification Technology Co., Ltd., Zhejiang Ant Micro Finance Services Group Co., Ltd., North
Jingyu Vision Technology Co., Ltd., Hangzhou Haoyuan Data Security Technology Co., Ltd., Guangdong Guangzhen Optoelectronics Technology Co., Ltd., Shenzhen Aikuzhi
Energy Technology Co., Ltd.
The main drafters of this standard. Huang Yuezhen, Lin Guanchen, Gao Jian, Yuan Yu, Wang Zhifei, Peng Cheng, Jin Xiaofeng, Liang Tiancai, Qin Rizhao, Nie Wei,
Zhang Liejun, Chen Guang, Wang Xiaoliang, Wang Xin, Liu Xudong, Chen Xing, Sun Yu, Gong Wenchuan, Xu Jun, Liu Bing, Zhang Xin, Jing Ning, Zhao Na, and Ke Wenhui.
Introduction
For some biometric application scenarios, it is difficult for them to satisfy their technical requirements by using a single biometric technology.
Energetic requirements, such as multiple personal ID checks, aviation security checks, etc. In addition, when it is impossible to provide certain biometric feature types
When reliable samples are provided, relevant regulations also need to be formulated.
Multimodal biometric measurement data is acquired using completely independent biosensors, identification algorithms, or multiple feature types. This is usually
Improve the technical performance of biometrics and reduce risk. The improvement in performance levels is that it does not require access to all biometrics
Instead of the measured value, the recognition result can be obtained based on only an arbitrary number of biometric measurements under the overall decision of the acceptance/rejection threshold.
The multimodal biometric recognition system is one of many types of biometric recognition systems, characterized in that each mode corresponds to its own
The same measurement method. At present, the achievement of fractional result fusion usually requires the real and false distribution information of biometrics.
Information Technology Biometrics
Multi-modality and other multi-biological fusion
1 Scope
This standard specifies multi-modality and other multi-biological feature fusion methods.
This standard applies to the integration of biometrics, including multiple biometric feature types, multiple instances, multiple sensors, multiple algorithms, decision-level and
Fractional logic.
2 Normative references
The following documents are indispensable for the application of this document. For dated references, only dated versions apply to this article
Pieces. For undated references, the latest version (including all amendments) applies to this document.
ISO /IEC 2382-37 Glossary of Information Technology - Part 37. Biometrics (Informationtechnology - Vo-
cabulary-Part 37.Biometrics)
ISO /IEC 29159-1 Information technology - Biometrics calibration, enhancement and fusion data - Part 1. Convergence information format
(Informationtechnology-Biometriccalibration, augmentationandfusiondata-Part 1. Fusioninfor-
mationformat-Part 1.BioAPI)
3 Terms and Definitions
The terms and definitions defined by ISO /IEC 2382-37 and the following apply to this document.
3.1
Biometric data source biometricdatasource
Information channels (such as sensors, feature types, algorithms, instances, or presentations) are the data processed by the fusion algorithm (eg, biometric recognition).
The origin of a set of samples, extracted features, alignment scores, rankings, or decisions.
3.2
Biometric process biometricprocess
An automated process of registration, verification or identification using one or more individual biometric features.
3.3
Biometric fusion biometricfusion
A combination of information from multiple sources (eg, sensors, modalities, algorithms, instances, or presentations, etc.).
3.4
Cascade system cascadedsystem
Use the pass/fail threshold of the biometric sample to determine if an overall systemic decision is needed to add additional biometrics
This system.
3.5
Layered system layeredsystem
A single biometric score is used to determine the pass/fail threshold for other biometric data.
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