GB/T 33767.9-2025 English PDFUS$379.00 · In stock
Delivery: <= 4 days. True-PDF full-copy in English will be manually translated and delivered via email. GB/T 33767.9-2025: Information technology - Biometric sample quality - Part 9: Vascular image data Status: Valid
Basic dataStandard ID: GB/T 33767.9-2025 (GB/T33767.9-2025)Description (Translated English): Information technology - Biometric sample quality - Part 9: Vascular image data Sector / Industry: National Standard (Recommended) Classification of Chinese Standard: L71 Classification of International Standard: 35.240.15 Word Count Estimation: 18,148 Date of Issue: 2025-04-25 Date of Implementation: 2025-11-01 Issuing agency(ies): State Administration for Market Regulation, China National Standardization Administration GB/T 33767.9-2025: Information technology - Biometric sample quality - Part 9: Vascular image data---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.ICS 35.240.15 CCSL71 National Standard of the People's Republic of China Information Technology Biometric Sample Quality Part 9.Vascular Image Data Part 9.Vascular image data 2025-11-01 Implementation State Administration for Market Regulation The National Standardization Administration issued Table of contentsPreface III Introduction IV 1 Scope 1 2 Normative references 1 3 Terms and Definitions 1 4 Vascular Image Quality 1 4.1 Overview 1 4.2 Vascular Image Quality Components 2 4.3 Unified Quality Score 8 5 Vascular Image Quality Data Recording 9 Reference 10ForewordThis document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents" Drafting. This document is part 9 of GB/T 33767 “Quality of Information Technology Biometric Samples”. GB/T 33767 has been published as follows. Lower part. --- Part 1.Framework; --- Part 4.Fingerprint image data; --- Part 5.Face image data; --- Part 6.Iris image data; --- Part 9.Vascular image data; --- Part 14.DNA data. Please note that some of the contents of this document may involve patents. The issuing organization of this document does not assume the responsibility for identifying patents. This document was proposed and coordinated by the National Technical Committee for Information Technology Standardization (SAC/TC28). This document was drafted by. Zhejiang University, China Electronics Technology Standardization Institute, China Electronics Technology Standardization Institute East China Branch, Harbin Engineering University, Heilongjiang University, Shengdian Century Technology Co., Ltd., Zhejiang Institute of Metrology, Tencent Technology (Shenzhen) Ltd., Zhejiang Sunny Optical Technology Co., Ltd., SenseTime Technology Co., Ltd., Beijing Sugon E-Tong Technology Co., Ltd., Tencent Technology Technology (Beijing) Co., Ltd., Hangzhou Hikvision Digital Technology Co., Ltd., Shanghai Institute of Metrology and Testing Technology, Lenovo Zhongtian Technology Co., Ltd., Shanxi Sanyouhe Wisdom Information Technology Co., Ltd., Beijing Eyesight Technology Co., Ltd., Wisdom Eye Technology Co., Ltd., Yimaitong (Shenzhen) Intelligent Technology Co., Ltd., Guangzhou Shoumai Intelligent Technology Co., Ltd., Qingdao Hitech Xinguang Technology Co., Ltd., Zhejiang University Huzhou Research Institute, China Media Capital Group Co., Ltd., Beijing University of Posts and Telecommunications, and Jiangsu CESI Technology Development Co., Ltd. The main drafters of this document are. Zheng Yinfei, Li Wei, Liu Qianying, Luo Zeyi, Song Jiwei, Wang Wenfeng, Wang Kejun, Ma Hui, Wang Jin, Li Xueshuang, Yao Lei, Song Xibin, Sun Shiyou, Sun Ronghua, Li Mengya, Li Xian, Zhong Chen, Luo Fuzhang, Lang Junqi, Li Yachun, Sun Rongrong, Zhang Jian, Pan Xiaoguang, Yang Chunlin, Pan Feng, Xue Xizhu, Xu Huabin, Liu Gaokai, Liu Yao, Zhu Lulu, Tan Dong, Song Fangfang, Wang Yaning, Zhou Chunhao.IntroductionGB/T 33767 "Quality of Information Technology Biometric Samples" aims to specify the quality requirements and The test method is planned to consist of 17 parts. --- Part 1.Framework. The purpose is to specify sample quality requirements and test methods for image data used in biometric recognition technology. General framework of law. --- Part 2.Fingerprint detail point data. The purpose is to specify the data used for fingerprint detail point recognition technology based on fingerprint detail points Sample quality requirements and test methods. --- Part 3.Fingerprint data. The purpose is to specify the sample of data used for fingerprint recognition technology based on fingerprints Quality requirements and test methods. --- Part 4.Fingerprint image data. The purpose is to provide a quantitative expression and interpretation of fingerprint image quality scores, quality analysis methods A combined approach of method and quality metrics. --- Part 5.Face image data. The purpose is to give the definition and classification of face image quality indicators, as well as the face image quality Analytical methods. --- Part 6.Iris image data. The purpose is to specify the definition and quantitative calculation method of iris image quality indicators, iris image Collection quality requirements and quality data recording coding format. --- Part 7.Signature/signature time series data. The purpose is to specify the signature/signature information used for signature/signature identification Sample quality requirements and testing methods for technical data. --- Part 8.Fingerprint skeleton data. The purpose is to specify the image data used for fingerprint skeleton recognition technology based on fingerprint skeleton patterns. Sample quality requirements and test methods based on the data. --- Part 9.Vascular image data. The purpose is to specify the sample of image data used for vascular recognition technology based on vascular images Quality measurement methods. --- Part 10.Hand shape contour data. The purpose is to specify image data based on hand shape contour images for hand shape recognition technology Sample quality requirements and test methods. --- Part 11.Dynamic data for signature/signature processing. The purpose is to specify the use of signature/signature behavior data based on the processed signature/signature behavior data. Sample quality requirements and test methods for data used in signature/signature recognition technology. --- Part 12.Face shape feature data. The purpose is to specify image data based on face shape feature data for face recognition technology Sample quality requirements and test methods. --- Part 13.Voiceprint data. The purpose is to specify the use of voiceprint data of a person based on a single speaker recorded in a single conversation. Sample quality requirements and testing methods for data used in speaker recognition technology. --- Part 14.DNA data. The purpose is to specify the DNA data types generated by high-throughput sequencing. Data Sample quality requirements and testing methods for data used in DNA identification technology. --- Part 15.Palmprint image data. The purpose is to specify the sample data of image data used for palmprint recognition technology based on palmprint images. This quality requirements and test methods. --- Part 16.Full body image data. The purpose is to specify the sample image data used for full body recognition technology based on full body images. This quality requirements and test methods. --- Part 17.Gait image sequence data. The purpose is to specify image data based on gait images for gait recognition technology Sample quality requirements and test methods. The quality of vascular image samples affects the performance of the system. Image quality evaluation can predict the system recognition performance (such as false matching rate, false mismatch rate, registration failure rate, and acquisition failure rate, etc.). Therefore, the quality information of vascular image data is crucial in many applications. GB/T 33767.1 assigns a quality domain to the quality score and specifies the allowable score range. The quality score is subdivided into Four categories, each corresponding to a qualitative explanation. The image is also defined in the standardized vascular image data exchange format of GB/T 26237.9. Quality Domain. Currently, there is a lack of definition of standard quality scores that would facilitate the representation, interpretation, and exchange of vascular image data. The aim is to express and explain the quality of vascular images in an objective and quantitative manner, and to evaluate the quality of vascular images collected from fingers, palms and backs of hands. measure. Information Technology Biometric Sample Quality Part 9.Vascular Image Data1 ScopeThis document describes a method for vascular image quality measurement and gives the composition of a vascular image quality data record. This document applies to the measurement of the quality of vascular image samples.2 Normative referencesThe contents of the following documents constitute essential clauses of this document through normative references in this document. For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to This document. GB/T 26237.9-2022 Information technology biometric identification data exchange format Part 9.Vascular image data GB/T 28826.2 Information technology common biometric identification exchange format framework Part 2.Biometric identification registration authority Operating procedures GB/T 33767.1 Information technology - Biometric sample quality - Part 1.Framework GB/T 45284.9-2025 Information technology - Extensible biometric data exchange format - Part 9.Vascular images data3 Terms and definitionsThe terms and definitions defined in GB/T 33767.1 and the following apply to this document. 3.1 foregroundregion The area within the finger outline or the whole hand outline in the hand blood vessel image used for identification. 3.2 centroid The center position of the foreground area in the hand blood vessel image.4 Vascular Image Quality4.1 Overview The vascular images are collected from the human hand. In this document, the finger vascular images, palm vascular images, and back vascular images are collected from the corresponding For venous images of the affected area, the standard posture and object coordinate system for image acquisition refer to the provisions of 7.8 in GB/T 26237.9-2022. Defects such as overexposure and blur in vascular images reduce image quality and affect the performance of the vascular recognition system. Causes of defects in vascular images Including the subject's physiological factors (such as skin condition), the subject's behavioral factors (such as collection posture), the collection equipment factors (such as light source or image Sensors, etc.) or environmental factors (such as ambient temperature and light, etc.). In the vascular image quality measurement, this document uses effective area, offset There are 9 quality components, including degree, contrast, equivalent visual number, information entropy, clarity, brightness uniformity, total length of blood vessels, and number of feature points. ......Tips & Frequently Asked Questions:Question 1: How long will the true-PDF of GB/T 33767.9-2025_English be delivered?Answer: Upon your order, we will start to translate GB/T 33767.9-2025_English as soon as possible, and keep you informed of the progress. The lead time is typically 2 ~ 4 working days. The lengthier the document the longer the lead time.Question 2: Can I share the purchased PDF of GB/T 33767.9-2025_English with my colleagues?Answer: Yes. The purchased PDF of GB/T 33767.9-2025_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet.Question 3: Does the price include tax/VAT?Answer: Yes. 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