GB/T 33767.5-2018 English PDFUS$359.00 · In stock
Delivery: <= 4 days. True-PDF full-copy in English will be manually translated and delivered via email. GB/T 33767.5-2018: Information technology -- Biometric sample quality -- Part 5: Face image data Status: Valid
Basic dataStandard ID: GB/T 33767.5-2018 (GB/T33767.5-2018)Description (Translated English): Information technology -- Biometric sample quality -- Part 5: Face image data Sector / Industry: National Standard (Recommended) Classification of Chinese Standard: L71 Classification of International Standard: 35.240.15 Word Count Estimation: 18,163 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 33767.5-2018: Information technology -- Biometric sample quality -- Part 5: Face 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.Information technology - Biometric sample quality - Part 5.Face image data ICS 35.240.15 L71 National Standards of People's Republic of China Information technology biometric sample quality Part 5.face image data (ISO /IEC TR29794-5.2010, NEQ) 2018-06-07 released 2019-01-01 Implementation State Administration of Market Supervision and Administration Issued by the National Standardization Administration of China Table of contentsForeword Ⅲ 1 Scope 1 2 Normative references 1 3 Terms and definitions 1 4 Abbreviations 1 5 Definition method of face image quality 2 6 Face image quality classification 2 7 Face image quality analysis 3 Appendix A (informative appendix) Symmetry analysis example 9 Reference 12ForewordGB/T 33767 "Quality of Information Technology Biometric Samples" is divided into the following parts. ---Part 1.Framework; ---Part 4.Fingerprint image data; ---Part 5.Face image data; ---Part 6.Iris image data. This part is Part 5 of GB/T 33767. This section was drafted in accordance with the rules given in GB/T 1.1-2009. This part uses the redrafting method to refer to ISO /IEC TR29794-5.2010 "Information Technology Biometric Sample Quality Part 5 Points. "Face Image Data" compiled, and the degree of consistency with ISO /IEC TR29794-5.2010 is not equivalent. Please note that some of the contents of this document may involve patents. The issuing agency of this document is not responsible for identifying these patents. This part is proposed and managed by the National Information Technology Standardization Technical Committee (SAC/TC28). Drafting organizations of this section. China Electronics Standardization Research Institute, Guangzhou Guangdian Zhuoshi Intelligent Technology Co., Ltd., Guangzhou Guangdian Express Rong Electronics Co., Ltd., Beijing Megvii Technology Co., Ltd., Beijing Tiancheng Shengye Technology Co., Ltd., Shenzhen Aiku Intelligent Technology Co., Ltd. Division, Hangzhou Shengyuan Data Security Technology Co., Ltd., Dongfang Netpower Technology Co., Ltd., Changchun Hongda Optoelectronics and Biostatistics Technology Co., Ltd., Shanxi Tiandi Technology Co., Ltd., Zhejiang Ant Small and Micro Financial Services Group Co., Ltd., Guangdong Optical Array Optoelectronics Branch Technology Co., Ltd., Shenzhen Saixi Information Technology Co., Ltd. The main drafters of this section. Huang Yuezhen, Luo Panfeng, Lin Guanchen, Gao Jian, Yuan Xin, Liang Tiancai, Nie Yunyun, Zhang Xin, Wang Xin, Liu Xudong, Liu Bing, Jin Xiaofeng, Zhang Liepi, Lu Xiaodong, Gong Wenchuan, Xu Jun, Wang Naizhou, Peng Cheng, Zheng Zheng, Chen Xing, Song Jiwei, Wang Wenfeng, Qin Rizhen. Information technology biometric sample quality Part 5.face image data1 ScopeThis part of GB/T 33767 specifies the definition, classification and analysis methods of face image quality indicators. This section applies to the analysis of face image quality.2 Normative referencesThe following documents are indispensable for the application of this document. For dated reference documents, only the dated version applies to this article Pieces. For undated reference documents, the latest version (including all amendments) is applicable to this document. GB/T 33767.1 Information Technology Biometric Sample Quality Part 1.Framework (GB/T 33767.1-2017, ISO /IEC 29794-1.2009, IDT) ISO /IEC 19794-5 Information Technology Biometric Data Exchange Format Part 5.Face Image Data (Information technology-Biometricdatainterchangeformats-Part 5.Faceimagedata) ISO /IEC 19794-5.2005/Amd.1 Information Technology Biometric Data Exchange Format Part 5.Face Image Data Modification 1.Face image data of conditionally taken photos (Informationtechnology-Biometricdatainterchangeformats- Part 5.Faceimagedata-Amendment1.Conditionsfortakingphotographsforfaceimagedata)3 Terms and definitionsThe following terms and definitions defined in GB/T 33767.1 apply to this document. 3.1 Comparison score A numerical value (or a set of values) obtained by comparison. 3.2 Face quality assessment algorithm facequalityassessmentalgorithm The algorithm used to calculate the quality of a given face image sample. 3.3 Facialimage Electronic image representation of human portraits.4 AbbreviationsThe following abbreviations apply to this document. CCD. Charge-Coupled Device (Charge-CoupledDevice) DCT. Discrete Cosine Transform (DiscreteCosineTransform) EXIF. Exchangeable Image File (ExchangeableImageFile) FQAA. Face Quality Assessment Algorithm (FaceQualityAssessmentAlgorithm)7 Face image quality analysis7.1 Overview Different factors should be considered when analyzing face image quality. These factors can be divided into. a) Image attributes, such as the size or resolution of the image; b) Image appearance characteristics, such as exposure or noise; c) Environmental characteristics, such as lighting or background; d) Features such as the consistency between the skin color displayed in the image and the subject's skin color; e) The subject's behavior. Some of the above attributes and characteristics are difficult to evaluate and evaluate, such as the matching degree between the skin color displayed in the image and the subject's skin color. See ISO /IEC 19794-5 for requirements of some attributes and characteristics such as eye distance (in pixels). The evaluation of these attributes and characteristics requires More complex algorithms and technologies in computer vision and image understanding. In addition, there may be different processing methods, such as automatic detection The position of the eyes in the face image is based on various principles. According to GB/T 33767.1, the regularized quality score can be obtained. The FQAA algorithm can detect images without segmenting the face area (for example, when measuring the image size to evaluate the static of the acquisition process). State features, such as compression rate, compression method, sensor resolution) or only analyze the face area (for example, estimate the pose of the subject). Locally In the region, the local structure of the face is defined by the pixel value (original or processed); a single quality score may be merged from multiple local results. Various face image quality evaluation algorithms can be developed for different environments, cameras, subjects and other factors, which are shown on different data sets. Different performance. 7.2 Dynamic subject characteristics 7.2.1 Subject behavior Common characteristics related to the subject's behavior include. ---Open your eyes; ---Open mouth; ---Various expressions, such as smiling or neutral; ---Head posture, such as facing directly or turning to any direction. Similar to environmental attributes or features, the quantification of the above parameters requires recognition of background, face and face features. In addition, the core algorithms (evaluating certain attributes) in the algorithm are required to be implemented on a computer, and the core algorithms can be obtained. The quantitative value of the evaluation performance, in order to achieve the purpose of reducing the computational complexity. If conditions permit, you can select the most commonly used algorithm or Read to reduce complexity. 7.2.2 Analysis based on the statistical difference between the left half of the face and the right half of the face 7.2.2.1 Illumination symmetry Assuming a two-dimensional portrait image (see the image specified in ISO /IEC 19794-5.2005/Amd.1), it can be divided by left and right symmetry. Analyze the quality of lighting and posture. The face area is divided into left and right areas based on the center line of the eyes (Figure 1). The following symmetry analysis is Detect the difference between the corresponding areas on the left and right of the face. The difference value represents the symmetry of a certain local image attribute, such as the original pixel value or the local filtered image. Prime value. The local image filter can use Gabor filter, local binary filter, sequential filter or any other suitable Local filter. The difference between the left and right regions gives the light quality score (i.e. how symmetrical the light is) or the attitude quality score (i.e. the frontal attitude). how). Most faces are symmetrical, but some people have obvious differences between the left and right parts of the face, such as marks, discoloration, etc. different. The symmetry of quality analysis measures should consider these differences. Note. Figure 1 is from the face image database, see reference [18]. The difference in illumination symmetry can be based on some local feature histograms HLm*n and HRm*n of the left and right face regions, where m is the feature vector The dimension of, n is the number of histogram groups. The histogram difference calculation formula is as follows. Di= HLm*n-HRm*n (1) In formula (1) |.| is a suitable form of histogram distance, such as histogram intersection, cross entropy or KL distance. The greater the difference, the greater the The worse the left-right symmetry, the lower the image quality in some respects. One possibility is to use the image to regularize the pixel values. A.1 gives an analysis example. 7.2.2.2 Attitude symmetry Pose symmetry is an important factor in the judgment of face image quality. The direction of face rotation is divided into up and down rotation angle (pitch) and depth rotation angle (yaw) and in-plane rotation angle (rol) three components, of which the change of the depth rotation angle component is the most important factor affecting the symmetry of the attitude Vegetarian. It is generally considered that a face image with a depth rotation angle component between -5° and 5° is a high-quality image, and the depth rotation angle component is between -30° and A face image between 30° is an acceptable image. The posture symmetry analysis should be based on the posture-sensitive image attributes. The local binary mode (LBP) can be used to filter pixel values. A.2 gives an analysis example. 7.3 Static characteristics of the acquisition process 7.3.1 Overview Typical scene characteristics describing environmental impact are as follows. ---Image enhancement and data compression process, such as image resolution and size; ---Static camera features, such as. resolution; ---Static features of the background, such as wallpaper. According to attributes or characteristics, quantification of these parameters requires the identification of background, face and facial features. Different core algorithms and their performance values can be used here. 7.3.2 Image resolution and size The number of image rows and columns of pixels can be used to indicate the nominal resolution. The interpupillary distance in pixels can be used to measure relative to the face The pixel range of the feature. In addition, using the statistical average of the interpupillary distance (for example, 63mm), the pixel density can be converted into a spatial sampling rate. 7.3.3 Noise 7.3.3.1 Noise source The noise in the face image comes from the various processes of acquiring the digital image. Different equipment or processes correspond to different introduced noises. phase Noise sources include. ---Digital image acquisition equipment, such as the image sensor of a digital camera; ---Analog image acquisition equipment; ---Image scanning equipment; ---Image compression algorithm, such as JPEG or wavelet compression. 7.3.3.2 Image acquisition noise The upper limit of image noise can be estimated by using the segmented smooth image prior model and measuring the response function of the CCD camera. 7.3.3.3 DCT compression noise Compressed artifacts caused by DCT compression can be estimated by the difference between the pixel gradient across the block boundary and the gradient of the internal pixels. To calculate the discontinuity of the boundary, the method of measuring the noise in the block can also be used. 7.4 Image acquisition characteristics 7.4.1 Image properties Metadata can be stored in the image according to different standards. Note. For example, digital cameras can store metadata information in EXIF format. EXIF is a "standard of image file format used by digital cameras". EXIF information contains the state of the camera when the information was captured. And some of the information (such as exposure time) is definitely helpful for quality assessment. in In the EXIF standard, different metadata tags are defined such as. data and time information, camera settings, exposure, location information, description and copyright information. The verification of image attributes does not need to rely on the metadata contained in the image source. 7.4.2 Image appearance The appearance of the image should depend on the color distribution of the image. ......Tips & Frequently Asked Questions:Question 1: How long will the true-PDF of GB/T 33767.5-2018_English be delivered?Answer: Upon your order, we will start to translate GB/T 33767.5-2018_English as soon as possible, and keep you informed of the progress. 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