WO2025050265A1 - Living body detection method based on structured light spot gradient energy - Google Patents
Living body detection method based on structured light spot gradient energy Download PDFInfo
- Publication number
- WO2025050265A1 WO2025050265A1 PCT/CN2023/116842 CN2023116842W WO2025050265A1 WO 2025050265 A1 WO2025050265 A1 WO 2025050265A1 CN 2023116842 W CN2023116842 W CN 2023116842W WO 2025050265 A1 WO2025050265 A1 WO 2025050265A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- image
- light spot
- spot
- light
- structured light
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/40—Spoof detection, e.g. liveness detection
Definitions
- the present invention relates to the technical field of liveness detection, and in particular to a liveness detection method based on structured light spot gradient energy.
- liveness detection based on structured light analysis. Specifically, this method first projects the speckle structured light image onto the detection target, and then uses a binocular camera or a monocular camera to detect the degree of deformation of the speckle structured light to determine the three-dimensional morphology of the object. This method can effectively detect printed photos or screen displays with planar structures, but it is difficult to deal with attacks from 3D masks or distorted photos.
- the purpose of the present invention is to solve the technical deficiencies in the security of existing face recognition systems and to provide a liveness detection method based on the gradient energy of structured light spots.
- the present invention performs clarity detection on each light spot in the structured light image, and utilizes the difference between the low clarity of the light spot in the living area and the high clarity of the light spot in the non-living area to innovate a liveness detection method based on structured light analysis.
- the present invention provides the following technical solution: a method for detecting living bodies based on structured light spot gradient energy, characterized in that it comprises the following steps:
- Locate all the light spots in the structured light image First, calculate the optimal pixel value threshold for distinguishing the light spots from the background. Then, binarize the image according to the threshold to obtain a mask highlighting the light spot area. Then, obtain the position information of all the light spots, including the center coordinates and the position of the smallest rectangle containing the corresponding area.
- this embodiment uses a near-infrared industrial camera with a resolution of 2592 x 1944 to capture images of the detection object under structured light irradiation, significantly enhancing the clarity difference between the light spots in the living area and the light spots in the non-living area, and designs an algorithm based on this to detect the clarity and position of the light spots in the image. If there is a living body, it can be determined that there is a living body in the image and the position of the living body can be given.
- Structured light spot detection module detects all spots with an area larger than 45 pixels in the structured light image and provides the location information of the spots.
- Structured light spot image clarity module uses the energy gradient function modified by the present invention to calculate the clarity of the spot image.
- Living area light spot detection module determines whether the light spot is in the living area based on the clarity of the light spot.
Landscapes
- Engineering & Computer Science (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
Description
本发明涉及活体检测技术领域，具体为基于结构光光斑梯度能量的活体检测方法。The present invention relates to the technical field of liveness detection, and in particular to a liveness detection method based on structured light spot gradient energy.
现有的人脸识别系统仍存在诸多安全隐患。由于人脸信息的易获取性,冒名顶替者可以简单地通过呈现合法用户的面部伪装来骗过人脸识别系统, 常见的人脸欺诈攻击方式包括打印人脸照片、屏幕播放人脸和戴 3维面具等。该行为不仅给用户的财产和隐私造成巨大的安全隐患,还给公共安全管理带来较大挑战。Existing face recognition systems still have many security risks. Due to the easy access to facial information, imposters can simply deceive face recognition systems by presenting a legitimate user's face disguise. Common face fraud attack methods include printing face photos, playing faces on the screen, and wearing 3D masks. This behavior not only poses a huge security risk to users' property and privacy, but also brings great challenges to public security management.
随着人脸识别技术在社会生产生活各行各业中越来越广泛的应用,为了保障人脸识别系统的安全性,发展人脸欺诈检测技术十分必要且迫切，研究人员已提出了大量相关技术,并有部分技术已经进入实际应用，其中一种常见的方法是基于结构光分析的活体检测，具体来说，该方法首先将散斑结构光图像投影到检测目标上，然后使用双目相机或者单目相机检测散斑结构光形变的程度以确定物体的三维形貌，该方法可以有效地检测具有平面结构的打印照片或者屏显,但是难以应对3维面具或者扭曲的照片的攻击。With the increasingly widespread application of face recognition technology in all walks of life and production, it is necessary and urgent to develop face fraud detection technology to ensure the security of face recognition systems. Researchers have proposed a large number of related technologies, and some of them have been put into practical application. One of the common methods is liveness detection based on structured light analysis. Specifically, this method first projects the speckle structured light image onto the detection target, and then uses a binocular camera or a monocular camera to detect the degree of deformation of the speckle structured light to determine the three-dimensional morphology of the object. This method can effectively detect printed photos or screen displays with planar structures, but it is difficult to deal with attacks from 3D masks or distorted photos.
本发明的目的在于解决现有人脸识别系统的安全性的技术不足，而提供基于结构光光斑梯度能量的活体检测方法，不同于现有的检测结构光图像整体形变的活体检测方法，对结构光图像中每个光斑进行清晰度检测，利用活体区域的光斑清晰度低和非活体区域的光斑清晰度高的差异，创新基于结构光分析的活体检测方法。The purpose of the present invention is to solve the technical deficiencies in the security of existing face recognition systems and to provide a liveness detection method based on the gradient energy of structured light spots. Different from the existing liveness detection method that detects the overall deformation of structured light images, the present invention performs clarity detection on each light spot in the structured light image, and utilizes the difference between the low clarity of the light spot in the living area and the high clarity of the light spot in the non-living area to innovate a liveness detection method based on structured light analysis.
为实现上述目的，本发明提供如下技术方案：基于结构光光斑梯度能量的活体检测方法，其特征在于，包括以下步骤：To achieve the above object, the present invention provides the following technical solution: a method for detecting living bodies based on structured light spot gradient energy, characterized in that it comprises the following steps:
S1、结构光图像获取，使用光学成像设备获取包含人体皮肤的结构光图像；S1. acquiring a structured light image, using an optical imaging device to acquire a structured light image containing human skin;
S2、定位结构光图像中所有的光斑，首先计算出用于区分光斑和背景的最优像素值阈值，然后依据该阈值对图像做二值化以获取光斑区域高亮的掩模，然后获取所有光斑的位置信息，包括中心坐标以及包含相应区域的最小矩形的位置；S2. Locate all the light spots in the structured light image. First, calculate the optimal pixel value threshold for distinguishing the light spots from the background. Then, binarize the image according to the threshold to obtain a mask highlighting the light spot area. Then, obtain the position information of all the light spots, including the center coordinates and the position of the smallest rectangle containing the corresponding area.
S3、挑选位于活体区域的光斑，首先遍历原图中的所有光斑图像，然后计算每个光斑图像的清晰度，最后挑选出清晰度低于预设值的光斑图像作为活体区域光斑的检测结果；S3, selecting the light spot located in the living body area, first traversing all the light spot images in the original image, then calculating the clarity of each light spot image, and finally selecting the light spot image with a clarity lower than a preset value as the detection result of the light spot in the living body area;
S4、在结构光图像中标出活体检测结果，将图像均匀分成N个子图，对子图进行有序搜索，有序搜索包括在子图中搜索步骤S3中检测到的活体区域的光斑，如果该子图中没有活体区域的光斑，则跳到下一个子图继续搜索，否则将该子图搜索到的第一个活体区域的光斑作为光斑集合的初始光斑，在初始光斑的邻域内搜索活体区域的光斑，将搜索到的光斑纳入光斑集合并且在这些光斑的邻域内开始下一轮搜索，重复将搜索到的光斑纳入光斑集合并且在这些光斑的邻域内开始下一轮搜索，直到搜索不到新的活体区域的光斑为止，并重复有序搜索直到遍历完所有的子图为止，最后比较获取的所有光斑集合，保留其中不重复的光斑集合，用不同的颜色在原图中标记保留的光斑集合。S4. Mark the liveness detection result in the structured light image, divide the image evenly into N sub-images, and perform orderly search on the sub-images. The orderly search includes searching the sub-image for the light spot of the live area detected in step S3. If there is no light spot of the live area in the sub-image, jump to the next sub-image to continue searching. Otherwise, use the first light spot of the live area searched in the sub-image as the initial light spot of the light spot set, search for the light spot of the live area in the neighborhood of the initial light spot, incorporate the searched light spots into the light spot set and start the next round of search in the neighborhood of these light spots, repeatedly incorporate the searched light spots into the light spot set and start the next round of search in the neighborhood of these light spots, until no new light spots of the live area are found, and repeat the orderly search until all sub-images are traversed. Finally, compare all the acquired light spot sets, retain the non-repeated light spot sets, and mark the retained light spot sets in the original image with different colors.
优选的，所述步骤S3中的能量梯度函数式为：Preferably, the energy gradient function in step S3 is:
（1） (1)
式中D代表图像的清晰度，f代表图像，h和w代表图像的高度和宽度。Where D represents the image clarity, f represents the image, and h and w represent the height and width of the image.
优选的，所述步骤S3中挑选的光斑图像清晰度数值需要根据结构光的亮度以及摄像头和人脸之间的距离整定。Preferably, the light spot image clarity value selected in step S3 needs to be adjusted according to the brightness of the structured light and the distance between the camera and the face.
优选的，所述步骤S4中光斑集合的初始光斑邻域大小为100 x 100个像素。Preferably, the initial spot neighborhood size of the spot set in step S4 is 100 x 100 pixels.
步骤S2中计算用于区分光斑和背景的最优像素值阈值的算法为大津阈值法。The algorithm for calculating the optimal pixel value threshold for distinguishing the light spot from the background in step S2 is the Otsu threshold method.
步骤S2中获取所有光斑的位置信息的方法是将获取光斑区域高亮的掩模输入到Matlab软件中的regionprops函数中计算得出。The method for obtaining the position information of all light spots in step S2 is to input the mask of the highlighted light spot area into the regionprops function in Matlab software for calculation.
步骤S3中遍历原图中的所有光斑图像，包含光斑区域的最小矩形内的图像和筛去面积小于45个像素的光斑图像。In step S3, all the spot images in the original image are traversed, including the images within the smallest rectangle of the spot area and the spot images with an area less than 45 pixels are screened out.
步骤S3中采用能量梯度函数式计算每个光斑图像的清晰度，最后挑选出清晰度低于900的光斑图像作为活体区域光斑的检测结果，当不存在符合条件的光斑或者符合条件的光斑不超过10个，则判定图中没有活体，并且在原图上标注出未检测出活体相关标记。In step S3, the clarity of each spot image is calculated using the energy gradient function, and finally the spot image with a clarity lower than 900 is selected as the detection result of the spot in the living area. When there is no spot that meets the conditions or there are no more than 10 spots that meet the conditions, it is determined that there is no living body in the image, and a mark related to no detection of living body is marked on the original image.
与现有技术相比，本发明的有益效果是：该基于结构光光斑梯度能量的活体检测方法克服了基于结构光分析的活体检测难以应对3维面具的攻击的缺陷，同时实现了在散斑结构光图像里对活体区域的定位。Compared with the prior art, the beneficial effects of the present invention are: the liveness detection method based on structured light spot gradient energy overcomes the defect that liveness detection based on structured light analysis is difficult to cope with the attack of 3D masks, and at the same time realizes the positioning of the living area in the speckle structured light image.
图1为活体区域和非活体区域的光斑清晰度比对示意图；FIG1 is a schematic diagram showing a comparison of light spot clarity between a living area and a non-living area;
图2为本发明的基于结构光光斑清晰度的活体检测方法流程图。FIG. 2 is a flow chart of a method for detecting living bodies based on structured light spot clarity of the present invention.
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，所描述的实施例仅仅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。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
请参阅图1-2，本发明提供的一种基于结构光光斑梯度能量的活体检测方法，用于在高清晰度照片和假人模型的干扰下准确地检测活体皮肤区域，包括以下步骤：Referring to FIG. 1-2 , the present invention provides a method for detecting a living body based on structured light spot gradient energy, which is used to accurately detect a living body skin area under the interference of a high-definition photo and a dummy model, and includes the following steps:
S1、结构光图像获取，使用光学成像设备获取包含人体皮肤的结构光图像，所述光学成像设备具有红外摄像头传感器，包含760-940 纳米近红外波段的监测功能，能够发射散斑结构光的光源，发射功率为180兆瓦，提供连续稳定的照明。S1. Structured light image acquisition: Use an optical imaging device to acquire a structured light image containing human skin. The optical imaging device has an infrared camera sensor, a monitoring function of the 760-940 nanometer near-infrared band, a light source capable of emitting speckle structured light, and an emission power of 180 mW, providing continuous and stable lighting.
为了实现对检测对象进行活体检测的目标，本实施例使用分辨率为2592 x 1944的近红外工业相机为结构光照射下的检测对象拍摄图像，显著增强活体区域的光斑和非活体区域的光斑的清晰度差异，并以此为基础设计算法检测图像中的光斑的清晰度和位置，如果存在活体，就可以判断图像中存在活体，并且给出活体的位置。In order to achieve the goal of liveness detection of the detection object, this embodiment uses a near-infrared industrial camera with a resolution of 2592 x 1944 to capture images of the detection object under structured light irradiation, significantly enhancing the clarity difference between the light spots in the living area and the light spots in the non-living area, and designs an algorithm based on this to detect the clarity and position of the light spots in the image. If there is a living body, it can be determined that there is a living body in the image and the position of the living body can be given.
S2、定位结构光图像中所有的光斑，首先用大津阈值法计算可以将光斑和背景区分开的最优像素值阈值，然后依据该阈值对图像做二值化以获取光斑区域高亮的掩模，然后将该掩模输入到Matlab软件中的regionprops函数获取所有光斑的位置信息，包括中心坐标以及包含相应区域的最小矩形的位置。S2. Locate all the light spots in the structured light image. First, use the Otsu threshold method to calculate the optimal pixel value threshold that can distinguish the light spots from the background. Then, binarize the image based on the threshold to obtain a mask that highlights the light spot area. Then, input the mask into the regionprops function in the Matlab software to obtain the location information of all the light spots, including the center coordinates and the position of the minimum rectangle containing the corresponding area.
S3、挑选位于活体区域的光斑，首先遍历原图中的所有光斑图像，包含光斑区域的最小矩形内的图像，筛去面积小于45个像素的光斑图像，然后采用能量梯度函数式计算每个光斑图像的清晰度，修改后的能量梯度函数的定义如式1所示：S3, select the light spot located in the living area, first traverse all the light spot images in the original image, including the image in the smallest rectangle of the light spot area, filter out the light spot images with an area less than 45 pixels, and then use the energy gradient function to calculate the clarity of each light spot image. The definition of the modified energy gradient function is shown in Formula 1:
（1） (1)
式中D代表图像的清晰度，f代表图像，h和w代表图像的高度和宽度，最后挑选出清晰度低于900（该参数需要根据结构光的亮度以及摄像头和人脸之间的距离进行整定）的光斑图像作为活体区域光斑的检测结果，当不存在符合条件的光斑或者符合条件的光斑不超过10个，则判定图中没有活体，并且在原图的顶部标记“此图中未检测出活体”。Where D represents the clarity of the image, f represents the image, h and w represent the height and width of the image. Finally, the spot image with a clarity lower than 900 (this parameter needs to be adjusted according to the brightness of the structured light and the distance between the camera and the face) is selected as the detection result of the spot in the living area. When there is no spot that meets the conditions or there are no more than 10 spots that meet the conditions, it is determined that there is no living thing in the image, and "No living thing was detected in this image" is marked at the top of the original image.
S4、在结构光图像中标出活体检测结果，将图像均匀分成N个子图，N为两个以上，优选分成16个子图，对子图进行有序搜索，有序搜索包括A.在子图中搜索步骤S3中检测到的活体区域的光斑，如果该子图中没有活体区域的光斑，则跳到下一个子图继续搜索，否则将该子图搜索到的第一个活体区域的光斑作为光斑集合的初始光斑，B.在初始光斑的邻域，如：初始光斑邻域大小为100 x 100个像素内搜索活体区域的光斑，C.将搜索到的光斑纳入光斑集合并且在这些光斑的邻域内开始下一轮搜索，D.重复C将搜索到的光斑纳入光斑集合并且在这些光斑的邻域内开始下一轮搜索，直到搜索不到新的活体区域的光斑为止，并重复有序搜索A、B、C、D直到遍历完所有的子图为止，最后比较获取的所有光斑集合，保留其中不重复的光斑集合，用不同的颜色在原图中标记保留的光斑集合。S4. Mark the liveness detection result in the structured light image, divide the image evenly into N sub-images, N is more than two, preferably divided into 16 sub-images, and perform orderly search on the sub-images. The orderly search includes A. searching the light spot of the live area detected in step S3 in the sub-image. If there is no light spot of the live area in the sub-image, jump to the next sub-image to continue searching. Otherwise, the light spot of the first live area searched in the sub-image is used as the initial light spot of the light spot set. B. In the neighborhood of the initial light spot, for example, the size of the neighborhood of the initial light spot is 100 x Search for light spots in the living area within 100 pixels, C. Add the searched light spots into the light spot set and start the next round of search in the neighborhood of these light spots, D. Repeat C to add the searched light spots into the light spot set and start the next round of search in the neighborhood of these light spots, until no new light spots in the living area are found, and repeat the orderly search A, B, C, D until all sub-graphs are traversed, and finally compare all the obtained light spot sets, retain the non-duplicate light spot sets, and mark the retained light spot sets in the original image with different colors.
还包括以下具体实施，采用实时分析从成像设备端采集的图像，包含的具体步骤如下：：The following specific implementation is also included, using real-time analysis of images collected from the imaging device, and the specific steps included are as follows:
a. 结构光光斑检测模块：检测结构光图像中所有面积大于45个像素的光斑，并且给出光斑的位置信息。a. Structured light spot detection module: detects all spots with an area larger than 45 pixels in the structured light image and provides the location information of the spots.
b. 结构光光斑图像清晰度模块：用本发明修改的能量梯度函数计算光斑图像的清晰度。b. Structured light spot image clarity module: uses the energy gradient function modified by the present invention to calculate the clarity of the spot image.
c. 活体区域光斑检测模块：根据光斑的清晰度判断该光斑是否在活体区域里。c. Living area light spot detection module: determines whether the light spot is in the living area based on the clarity of the light spot.
d．UI界面：将拍摄的结构光图像显示在界面上，并且在界面上标记检测结果，如果检测到10个以上的活体区域的光斑，则在界面上标出所有活体区域的光斑的位置，否则在界面的原图上标注出未检测出活体相关标记，比如在原图顶部标出“此图中未检测出活体”。d. UI interface: Display the captured structured light image on the interface and mark the detection results on the interface. If more than 10 light spots of living areas are detected, the positions of the light spots of all living areas are marked on the interface. Otherwise, the original image of the interface is marked with relevant marks indicating that no living body is detected, such as marking "No living body is detected in this image" at the top of the original image.
对于本领域技术人员而言，显然本发明不限于上述示范性实施例的细节，而且在不背离本发明的精神或基本特征的情况下，能够以其他的具体形式实现本发明。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本发明内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2023/116842 WO2025050265A1 (en) | 2023-09-04 | 2023-09-04 | Living body detection method based on structured light spot gradient energy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2023/116842 WO2025050265A1 (en) | 2023-09-04 | 2023-09-04 | Living body detection method based on structured light spot gradient energy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2025050265A1 true WO2025050265A1 (en) | 2025-03-13 |
Family
ID=94922738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2023/116842 Pending WO2025050265A1 (en) | 2023-09-04 | 2023-09-04 | Living body detection method based on structured light spot gradient energy |
Country Status (1)
| Country | Link |
|---|---|
| WO (1) | WO2025050265A1 (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170286788A1 (en) * | 2016-04-01 | 2017-10-05 | Beijing Kuangshi Technology Co., Ltd. | Liveness detection method, liveness detection system, and computer program product |
| CN113255587A (en) * | 2021-06-24 | 2021-08-13 | 深圳市光鉴科技有限公司 | Face-brushing payment system based on depth camera |
| CN113468920A (en) * | 2020-03-31 | 2021-10-01 | 深圳市光鉴科技有限公司 | Living body detection method, system, equipment and medium based on face light spot image |
| WO2023060756A1 (en) * | 2021-10-13 | 2023-04-20 | 深圳前海微众银行股份有限公司 | Face anti-spoofing detection method and device, and readable storage medium and computer program product |
| CN116188588A (en) * | 2022-11-11 | 2023-05-30 | 苏州全视智能光电有限公司 | Light spot center positioning method and device |
| CN116224531A (en) * | 2023-02-27 | 2023-06-06 | 上海航天控制技术研究所 | Focusing device and method for rapid intelligent high-precision infrared imaging lens |
-
2023
- 2023-09-04 WO PCT/CN2023/116842 patent/WO2025050265A1/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170286788A1 (en) * | 2016-04-01 | 2017-10-05 | Beijing Kuangshi Technology Co., Ltd. | Liveness detection method, liveness detection system, and computer program product |
| CN113468920A (en) * | 2020-03-31 | 2021-10-01 | 深圳市光鉴科技有限公司 | Living body detection method, system, equipment and medium based on face light spot image |
| CN113255587A (en) * | 2021-06-24 | 2021-08-13 | 深圳市光鉴科技有限公司 | Face-brushing payment system based on depth camera |
| WO2023060756A1 (en) * | 2021-10-13 | 2023-04-20 | 深圳前海微众银行股份有限公司 | Face anti-spoofing detection method and device, and readable storage medium and computer program product |
| CN116188588A (en) * | 2022-11-11 | 2023-05-30 | 苏州全视智能光电有限公司 | Light spot center positioning method and device |
| CN116224531A (en) * | 2023-02-27 | 2023-06-06 | 上海航天控制技术研究所 | Focusing device and method for rapid intelligent high-precision infrared imaging lens |
Non-Patent Citations (2)
| Title |
|---|
| BYTE CAT: "Clarity detection and enhancement algorithm", JIANSHU, XP009562143, Retrieved from the Internet <URL:https://www.jianshu.com/p/43b210425755> * |
| HAN YA-RONG, HUANG CHAO-BING, ZHONG JI-WEI, WANG XIANG: "A Method of Thresholding Segmentation and Spot Center Coordinates Calculation for Light Spot-Image", MODERN COMPUTER, 25 September 2011 (2011-09-25), CN, pages 13 - 16, XP093287259, ISSN: 2097-597X * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106446873B (en) | Face detection method and device | |
| CN107705288B (en) | Infrared video detection method of dangerous gas leakage under strong interference of false target movement | |
| CA2867365C (en) | Method, system and computer storage medium for face detection | |
| JP4970195B2 (en) | Person tracking system, person tracking apparatus, and person tracking program | |
| CN106530310B (en) | A kind of pedestrian count method and device based on the identification of human body overhead | |
| KR100480781B1 (en) | Method of extracting teeth area from teeth image and personal identification method and apparatus using teeth image | |
| TW202011733A (en) | Method and device for target sampling of image | |
| US20130243343A1 (en) | Method and device for people group detection | |
| CN101246544A (en) | Iris positioning method based on boundary point search and SUSAN edge detection | |
| CN104574365B (en) | Obstacle detector and method | |
| US10650249B2 (en) | Method and device for counting pedestrians based on identification of head top of human body | |
| CN111739029B (en) | Deep learning convolutional neural network-based electric bucket tooth falling detection method | |
| CN107657639A (en) | A kind of method and apparatus of quickly positioning target | |
| CN102096802A (en) | Face detection method and device | |
| CN108921881A (en) | A kind of across camera method for tracking target based on homography constraint | |
| CN107967687A (en) | A method and system for acquiring the walking posture of a target object | |
| WO2021217764A1 (en) | Human face liveness detection method based on polarization imaging | |
| CN105574509A (en) | Face identification system playback attack detection method and application based on illumination | |
| CN111582118A (en) | Face recognition method and device | |
| CN107067595A (en) | State identification method, device and the electronic equipment of a kind of indicator lamp | |
| CN111222432A (en) | Face living body detection method, system, equipment and readable storage medium | |
| CN104915641B (en) | The method that facial image light source orientation is obtained based on Android platform | |
| WO2025050265A1 (en) | Living body detection method based on structured light spot gradient energy | |
| CN119380042B (en) | An event feature extraction method based on spatial gradient variance maximization | |
| CN112926516B (en) | Robust finger vein image region-of-interest extraction method |
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: 23951094 Country of ref document: EP Kind code of ref document: A1 |